'''

I converted the idlastro function to python, I changed the name of Wave to

microns and I changed the input to be Av instead of E(B-V).

pro ccm_UNRED, wave, flux, ebv, funred, R_V = r_v

;+

; NAME:

; CCM_UNRED

; PURPOSE:

; Deredden a flux vector using the CCM 1989 parameterization

; EXPLANATION:

; The reddening curve is that of Cardelli, Clayton, and Mathis (1989 ApJ.

; 345, 245), including the update for the near-UV given by O'Donnell

; (1994, ApJ, 422, 158). Parameterization is valid from the IR to the

; far-UV (3.5 microns to 0.1 microns).

;

; Users might wish to consider using the alternate procedure FM_UNRED

; which uses the extinction curve of Fitzpatrick (1999).

; CALLING SEQUENCE:

; CCM_UNRED, wave, flux, ebv, funred, [ R_V = ]

; or

; CCM_UNRED, wave, flux, ebv, [ R_V = ]

; INPUT:

; WAVE - wavelength vector (Angstroms)

; FLUX - calibrated flux vector, same number of elements as WAVE

; If only 3 parameters are supplied, then this vector will

; updated on output to contain the dereddened flux.

; EBV - color excess E(B-V), scalar. If a negative EBV is supplied,

; then fluxes will be reddened rather than deredenned.

;

; OUTPUT:

; FUNRED - unreddened flux vector, same units and number of elements

; as FLUX

;

; OPTIONAL INPUT KEYWORD

; R_V - scalar specifying the ratio of total selective extinction

; R(V) = A(V) / E(B - V). If not specified, then R_V = 3.1

; Extreme values of R(V) range from 2.75 to 5.3

;

; EXAMPLE:

; Determine how a flat spectrum (in wavelength) between 1200 A and 3200 A

; is altered by a reddening of E(B-V) = 0.1. Assume an "average"

; reddening for the diffuse interstellar medium (R(V) = 3.1)

;

; IDL> w = 1200 + findgen(40)*50 ;Create a wavelength vector

; IDL> f = w*0 + 1 ;Create a "flat" flux vector

; IDL> ccm_unred, w, f, -0.1, fnew ;Redden (negative E(B-V)) flux vector

; IDL> plot,w,fnew

;

; NOTES:

; (1) The CCM curve shows good agreement with the Savage & Mathis (1979)

; ultraviolet curve shortward of 1400 A, but is probably

; preferable between 1200 and 1400 A.

; (2) Many sightlines with peculiar ultraviolet interstellar extinction

; can be represented with a CCM curve, if the proper value of

; R(V) is supplied.

; (3) Curve is extrapolated between 912 and 1000 A as suggested by

; Longo et al. (1989, ApJ, 339,474)

; (4) Use the 4 parameter calling sequence if you wish to save the

; original flux vector.

; (5) Valencic et al. (2004, ApJ, 616, 912) revise the ultraviolet CCM

; curve (3.3 -- 8.0 um-1). But since their revised curve does

; not connect smoothly with longer and shorter wavelengths, it is

; not included here.

;

; REVISION HISTORY:

; Written W. Landsman Hughes/STX January, 1992

; Extrapolate curve for wavelengths between 900 and 1000 A Dec. 1993

; Use updated coefficients for near-UV from O'Donnell Feb 1994

; Allow 3 parameter calling sequence April 1998

; Converted to IDLV5.0 April 1998

;-

'''

x = 1./ microns # Convert to inverse microns

a = np.empty_like(x)

b = np.empty_like(x)

good = np.logical_and( x > 0.3,x < 1.1 )#Infrared

if good.sum()>0:

a[good] = 0.574 * x[good]**(1.61)

b[good] = -0.527 * x[good]**(1.61)

good = np.logical_and( x >= 1.1, x < 3.3) #Optical/NIR

if good.sum()>0: #Use new constants from O'Donnell (1994)

y = x[good] - 1.82

#c1 = [ 1. , 0.17699, -0.50447, -0.02427, 0.72085, $ ;Original

# 0.01979, -0.77530, 0.32999 ] ;coefficients

#c2 = [ 0., 1.41338, 2.28305, 1.07233, -5.38434, $ ;from CCM89

# -0.62251, 5.30260, -2.09002 ]

c1 = [ 1. , 0.104, -0.609, 0.701, 1.137, \

-1.718, -0.827, 1.647, -0.505 ] #from O'Donnell (1994)

c2 = [ 0., 1.952, 2.908, -3.989, -7.985, \

11.102, 5.491, -10.805, 3.347 ]

a[good] = np.polyval(c1[::-1], y)#numpy.polyval likes backwards coefficients...

b[good] = np.polyval(c2[::-1], y)

good = np.logical_and( x >= 3.3,x < 8) #Mid-UV

if good.sum()>0:

y = x[good]

F_a = np.zeros_like(y)

F_b = np.zeros_like(y)

good1 = y > 5.9

if good1.sum()>0:

y1 = y[good1] - 5.9

F_a[ good1] = -0.04473 * y1**2 - 0.009779 * y1**3

F_b[ good1] = 0.2130 * y1**2 + 0.1207 * y1**3

a[good] = 1.752 - 0.316*y - (0.104 / ( (y-4.67)**2 + 0.341 )) + F_a

b[good] = -3.090 + 1.825*y + (1.206 / ( (y-4.62)**2 + 0.263 )) + F_b

good = np.logical_and(x >= 8,x < 11 ) #Far-UV

if good.sum()>0:

y = x[good] - 8.

c1 = [ -1.073, -0.628, 0.137, -0.070 ]

c2 = [ 13.670, 4.257, -0.420, 0.374 ]

a[good] = np.polyval(c1[::-1],y)

b[good] = np.polyval(c2[::-1],y)

A_lambda = Av * (a + b/Rv)

'''

Inputs:

w_arr - [1d array] an array of wavelengths

windows - [list of tuples] each tuple defines the lower and upper

extent (in units of w_arr) of a window

Returns:

1d array - indices of w_arr which are within windows are set to 1

0 otherwise

'''

inds=[]

for interval in windows:

inds.append(np.logical_and(w_arr>interval[0],w_arr<interval[1]))

template_glob='phoenix_model*.dat',windows=[(0,np.inf)]):

'''

Determine the necessary shift in wavelength necessary to align a template spectrum

and an observed spectrum

Inputs:

obs_spec -[str] filename of observed spectrum (col0 is wavelength, col1 is flux)

model_spec_path -[str] the path to the photospheric spectra files. Each file in this

dir should have col0 wavelength and col1 flux

shift_stepsize -[float] the step size to use while gridding (only steps in one

direction...)

shift_total_size-[float] the total amount to shift during the fitting process.

template_globe -[str] a glob string that identifies all of the desired template

datafiles in model_spec_path

windows -[list of tuples] to be fed to analysis.make_inds. These are

windows around spectral features that the fitter will use

while generating it's figure of merit value...

Returns:

None - prints the best fit model name and shift

'''

spec=readcol(obs_spec,colNames=['w','f'])

inds=make_inds(spec['w'],windows)

model_photospheres={}

fnames=sorted(fnfilter(listdir(model_spec_path),'phoenix_model*.dat'))

print len(fnames)

for f in fnames:

model_photospheres[f]=readcol(model_spec_path+f,colNames=['w','f'])

pm=np.polyfit(model_photospheres[f]['w'],model_photospheres[f]['f'],3)

polynomial=np.polyval(pm,model_photospheres[f]['w'])

polynomial-=polynomial.mean()

model_photospheres[f]['f']-=polynomial

fit_dict={}

for shift in np.arange(0,shift_total_size,shift_stepsize):

for model in fnames:

model_func=interp1d(model_photospheres[model]['w']+shift,model_photospheres[model]['f'])

diff=(spec['f']-model_func(spec['w']))**2

fit_dict[(model,shift)]=diff[inds].sum()

skeys=sorted(fit_dict,key=fit_dict.get)

'''return the equivalent width of a spectral feature inside

the provided integration limits. The continuum level is

calculated as the mean outside the integration limits.

Inputs:

obs_spec -[len=2 tuple] zeroeth element is the wavelength array

first element is the flux array

int_limits -[len=2 tuple] the lower and upper limits (in wavelength

units).

Returns:

float -the equivalent width (in the same units as the wavelength

array) of the spectral feature within int_limits...

'''

w=obs_spec[0]

f=obs_spec[1]

inside=np.logical_and(w>int_limits[0],w<int_limits[1])

f0=f[~inside].mean()

'''return the equivalent width of a spectral feature inside

the provided integration limits. The continuum level is

calculated as the median outside the integration limits.

Inputs:

obs_spec -[len=2 tuple] zeroeth element is the wavelength array

first element is the flux array

int_limits -[len=2 tuple] the lower and upper limits (in wavelength

units).

Returns:

float -the equivalent width (in the same units as the wavelength

array) of the spectral feature within int_limits...

'''

w=obs_spec[0]

f=obs_spec[1]

inside=np.logical_and(w>int_limits[0],w<int_limits[1])

f0=np.median(f[~inside])