''' this function exports the spectra corresponding to the best fits for a list of targets'''

from hyperion.model import ModelOutput

from hyperion.dust import SphericalDust

import numpy as np

import matplotlib.pyplot as plt

from astropy.table import Table

import pickle

from hyperion.util.constants import pc

target_list = ['IRAS20050.1','IRAS20050.2','IRAS20050.3','IRAS20050.4','IRAS20050.5']

dist = 700*pc

folder = ['Grid/']

name = ['model']

angles=np.arccos(np.linspace(0,1.,20))*180./np.pi

inclinations=angles[::-1]

d = SphericalDust()

d.read('d03_5.5_3.0_A.hdf5')

chi = d.optical_properties.chi

chi = chi[::-1]

wav = d.optical_properties.wav

wav = wav[::-1]

Chi = interp1d(wav,chi,kind='linear')

sorted_grid = pickle.load(open(folder[0]+name[0]+"_"+target+".grid.dat",'r'))

best_model_fname = folder[0]+sorted_grid['name'][0]+'.rtout'

best_model = ModelOutput(fname)

inc = int(np.argwhere(inclinations==sorted_grid['inc'][0]))

sed = best_model.get_sed(aperture=-1, inclination=inc, distance=dist,units='Jy')

N = len(sed.wav)

vec = np.zeros(N,len(target_list)+1)

vec[:,0] = sed.wav

for i in range(len(target_list)):

target = target_list[i]

sorted_grid = pickle.load(open(folder[0]+name[0]+"_"+target+".grid.dat",'r'))

best_model_fname = folder[0]+sorted_grid['name'][0]+'.rtout'

best_model = ModelOutput(fname)

extinction = sorted_grid['ext'][0]

# get inclination

inc = int(np.argwhere(inclinations==sorted_grid['inc'][0]))

# get sed for best fit

sed = best_model.get_sed(aperture=-1, inclination=inc, distance=dist,units='Jy')

tau_ext1 = Chi(sed.wav)/Chi(0.550)/1.086

tau = tau_ext1*extinction

#print "tau,",tau

# calculate extinction for all inclinations

ext = np.exp(-tau)#np.array([np.exp(-tau) for shape in range(sed.val.shape[0])])

#print "ext,",ext

# apply extinction to model

extinct_values = sed.val*ext

vec[:,i+1] = extinct_values

print vec