Set defaults of this module

If you give no keyword arguments, the default values will be reset.

Return a list of available grid names.

Returns: list of str

list of grid names

Retrieve the possible effective temperatures and gravities from a grid.

Returns: (ndarray,ndarray)

effective temperatures, gravities

Return InterpolatingFunction spanning the available grid of atmosphere models.

WARNING: the grid must be entirely defined on a mesh grid, but it does not need to be equidistant.

It is thus the user's responsibility to know whether the grid is evenly spaced in logg and teff (e.g. this is not so for the CMFGEN models).

You can supply your own wavelength range, since the grid models' resolution are not necessarily homogeneous. If not, the first wavelength array found in the grid will be used as a template.

It might take a long a time and cost a lot of memory if you load the entire grid. Therefor, you can also set range of temperature and gravity.

Parameters:

• wave (ndarray) - wavelength to define the grid on
• teffrange (tuple of floats) - starting and ending of the grid in teff
• loggrange (tuple of floats) - starting and ending of the grid in logg

Returns: (3x1Darray,3Darray,interp_func)

wavelengths, teffs, loggs and fluxes of grid, and the interpolating function

Decorators:

• @memoized

Retrieve the filename containing the specified SED grid.

The keyword arguments are specific to the kind of grid you're using.

Basic keywords are 'grid' for the name of the grid, and 'z' for metallicity. For other keywords, see the source code.

Available grids and example keywords:

• grid='kurucz93': * metallicity (z): m01 is -0.1 log metal abundance relative to solar (solar abundances from Anders and Grevesse 1989) * metallicity (z): p01 is +0.1 log metal abundance relative to solar (solar abundances from Anders and Grevesse 1989) * alpha enhancement (alpha): True means alpha enhanced (+0.4) * turbulent velocity (vturb): vturb in km/s * nover= True means no overshoot * odfnew=True means no overshoot but with better opacities and abundances

Parameters:

• integrated (boolean) - choose integrated version of the grid
• grid (str) - gridname (default Kurucz)

Returns: str

gridfile

Retrieve the specific intensity of a model atmosphere.

ebv is reddening vrad is radial velocity: positive is redshift, negative is blueshift (km/s!)

extra kwargs are for reddening

You get limb angles, wavelength and a table. The shape of the table is (N_wave,N_mu).

WARNING: wave and flux units cannot be specificed for the moment.

>>> mu,wave,table = get_table(10000,4.0)

>>> p = pl.figure()
>>> ax1 = pl.subplot(221)
>>> p = pl.title('E(B-V)=0, vrad=0')
>>> ax = pl.gca()
>>> ax.set_color_cycle([pl.cm.spectral(i) for i in np.linspace(0,1,len(mu))])
>>> p = pl.loglog(wave,table)
>>> p = pl.xlim(700,15000)
>>> p = pl.ylim(1e3,1e8)

>>> mu,wave,table = get_table(10000,4.0,ebv=0.5)

>>> p = pl.subplot(222,sharex=ax1,sharey=ax1)
>>> p = pl.title('E(B-V)=0.5, vrad=0')
>>> ax = pl.gca()
>>> ax.set_color_cycle([pl.cm.spectral(i) for i in np.linspace(0,1,len(mu))])
>>> p = pl.loglog(wave,table)
>>> p = pl.xlim(700,15000)
>>> p = pl.ylim(1e3,1e8)

>>> mu,wave,table = get_table(10000,4.0,vrad=-1000.)

>>> p = pl.subplot(223,sharex=ax1,sharey=ax1)
>>> p = pl.title('E(B-V)=0., vrad=-10000.')
>>> ax = pl.gca()
>>> ax.set_color_cycle([pl.cm.spectral(i) for i in np.linspace(0,1,len(mu))])
>>> p = pl.loglog(wave,table)
>>> p = pl.xlim(700,15000)
>>> p = pl.ylim(1e3,1e8)

>>> mu,wave,table = get_table(10000,4.0,vrad=-1000.,ebv=0.5)

>>> p = pl.subplot(224,sharex=ax1,sharey=ax1)
>>> p = pl.title('E(B-V)=0.5, vrad=-10000.')
>>> ax = pl.gca()
>>> ax.set_color_cycle([pl.cm.spectral(i) for i in np.linspace(0,1,len(mu))])
>>> p = pl.loglog(wave,table)
>>> p = pl.xlim(700,15000)
>>> p = pl.ylim(1e3,1e8)

>>> mu,wave,table = get_table(10050,4.12)

>>> p = pl.figure()
>>> pl.gca().set_color_cycle([pl.cm.spectral(i) for i in np.linspace(0,1,len(mu))])
>>> p = pl.loglog(wave,table)

Parameters:

• teff (float) - effective temperature (K)
• logg (float) - log surface gravity (cgs, dex)
• ebv (float) - reddening (mag)
• vrad (float) - radial velocity (for doppler shifting) (km/s)

Returns: array, array, array

mu angles, wavelengths, table (Nwave x Nmu)

Retrieve a limb darkening law for a specific star and specific bandpass.

Possibility to include reddening via EB-V parameter. If not given, reddening will not be performed...

You choose your own reddening function.

See e.g. Heyrovsky et al., 2007

If you specify one angle (mu in radians), it will take the closest match from the grid.

Mu = cos(theta) where theta is the angle between the surface and the line of sight. mu=1 means theta=0 means center of the star.

Example usage:

>>> teff,logg = 5000,4.5
>>> mu,intensities = get_limbdarkening(teff=teff,logg=logg,photbands=['JOHNSON.V'])

Parameters:

• teff (float) - effective temperature
• logg (float) - logarithmic gravity (cgs)
• system (string) - bandpass system
• photbands (list of strings) - bandpass filters
• ebv (float) - reddening coefficient
• vrad (float) - radial velocity (+ is redshift, - is blueshift)
• mu (float) - specificy specific angle

Retrieve limb darkening coefficients on the fly.

This is particularly useful if you only need a few and speed is not really a problem (although this function is nearly instantaneous, looping over it will expose that it's actually pretty slow).

Parameters:

• teff (float) - effective temperature
• logg (float) - logarithmic gravity (cgs)
• photbands (list of strings) - bandpass filters
• ebv (float) - reddening coefficient
• vrad (float) - radial velocity (+ is redshift, - is blueshift)

Calculate the intensity moment (see Townsend 2003, eq. 39).

Test analytical versus numerical implementation:

#>>> photband = 'JOHNSON.V' #>>> l,logg = 2.,4.0 #>>> gridfile = 'tables/ikurucz93_z0.0_k2_ld.fits' #>>> mu = np.linspace(0,1,100000) #>>> P_l = legendre(l)(mu) #>>> check = [] #>>> for teff in np.linspace(9000,12000,19): #... a1x,a2x,a3x,a4x,I_x1 = get_itable(gridfile,teff=teff,logg=logg,mu=1,photband=photband,evaluate=False) #... coeffs = [a1x,a2x,a3x,a4x,I_x1] #... Ix_mu = ld_claret(mu,coeffs) #... Ilx1 = I_x1*np.trapz(P_l*mu*Ix_mu,x=mu) #... a0x = 1 - a1x - a2x - a3x - a4x #... limb_coeffs = [a0x,a1x,a2x,a3x,a4x] #... Ilx2 = 0 #... for r,ar in enumerate(limb_coeffs): #... s = 1 + r/2. #... myIls = _I_ls(l,s) #... Ilx2 += ar*myIls #... Ilx2 = I_x1*Ilx2 #... Ilx3 = intensity_moment(teff,logg,photband,coeffs) #... check.append(abs(Ilx1-Ilx2)<0.1) #... check.append(abs(Ilx1-Ilx3)<0.1) #>>> np.all(np.array(check)) #True

Parameters:

• teff (float) - effecitve temperature (K)
• logg (float) - log of surface gravity (cgs)
• ll (float) - degree of the mode
• photband (list of strings ( or iterable container)) - photometric passbands
• full_output (boolean) - if True, returns intensity, coefficients and integrals separately

Returns:

intensity moment

Fit an LD law to a sampled set of limb angles/intensities.

In my (Pieter) experience, C{fitmethod='equidist_r_leastsq' seems
appropriate for the Kurucz models.

Make sure the intensities are normalised!

>>> mu,intensities = get_limbdarkening(teff=10000,logg=4.0,photbands=['JOHNSON.V'],normalised=True)
>>> p = pl.figure()
>>> p = pl.plot(mu,intensities[:,0],'ko-')
>>> cl,ssr,rdiff = fit_law(mu,intensities[:,0],law='claret')

>>> mus = np.linspace(0,1,1000)
>>> Imus = ld_claret(mus,cl)
>>> p = pl.plot(mus,Imus,'r-',lw=2)



@return: coefficients, sum of squared residuals,relative flux difference between prediction and model integrated intensity
@rtype: array, float, float

Gets the grid and fits LD law to all the models.

Does not use mu=0 point in the fitting process.

Retrieve an interpolating grid for the LD coefficients

Check outcome:

#>>> bands = ['GENEVA.U', 'GENEVA.B', 'GENEVA.G', 'GENEVA.V'] #>>> f_ld_grid = get_ld_grid(bands) #>>> ff = pf.open(_atmos['file']) #>>> all(ff['GENEVA.U'].data[257][2:]==f_ld_grid(ff['GENEVA.U'].data[257][0],ff['GENEVA.U'].data[257][1])[0:5]) #True #>>> all(ff['GENEVA.G'].data[257][2:]==f_ld_grid(ff['GENEVA.G'].data[257][0],ff['GENEVA.G'].data[257][1])[10:15]) #True #>>> ff.close()

#Make some plots:

#>>> photband = ['GENEVA.V'] #>>> f_ld = get_ld_grid(photband) #>>> logg = 4.0 #>>> mu = linspace(0,1,100) #>>> p = figure() #>>> p = gcf().canvas.set_window_title('test of function <get_ld_grid>') #>>> for teff in linspace(9000,12000,19): #... out = f_ld(teff,logg) #... a1x,a2x,a3x,a4x, I_x1 = out.reshape((len(photband),5)).T #... p = subplot(221);p = title('Interpolation of absolute intensities') #... p = plot(teff,I_x1,'ko') #... p = subplot(222);p = title('Interpolation of LD coefficients') #... p = scatter(4*[teff],[a1x,a2x,a3x,a4x],c=range(4),vmin=0,vmax=3,cmap=cm.spectral,edgecolors='none') #... p = subplot(223);p = title('Without absolute intensity') #... p = plot(mu,ld_eval(mu,[a1x,a2x,a3x,a4x]),'-') #... p = subplot(224);p = title('With absolute intensity') #... p = plot(mu,I_x1*ld_eval(mu,[a1x,a2x,a3x,a4x]),'-')

Decorators:

• @memoized

defaults

Value:

dict(grid= 'kurucz', odfnew= False, z=+ 0.0, vturb= 2, alpha= False, n over= False)