print("done")
vsini=np.asarray(vsini)
labels = np.vstack((teff,rstar,feh,vsini)).T
# pickle things
pickling_on = open('cannon2_specmatch_labels_snr' + str(target_snr) + '.pkl', "wb")
pickle.dump(labels,pickling_on)
# ---- normalizes spectra via Gaussian filter -----------------
# training set
spectra_tr_norm = []
for spectrum1,val1 in zip(spectra_tr,err_tr):
spectrum1,val1 = gauss_norm(spectrum1,lib.wav,val1,3)
spectra_tr_norm.append(spectrum1)
spectra_tr_norm = np.asarray(spectra_tr_norm)
# validation set
spectra_val_norm = []
for spectrum2,val2 in zip(spectra_val,err_val):
spectrum2,val2 = gauss_norm(spectrum2,lib.wav,val2,3)
spectra_val_norm.append(spectrum2)
spectra_val_norm = np.asarray(spectra_val_norm)
# --- remove Na doublet (not a good feature to fit - activity-driven)
na_min = 5887
lib_path = input('Enter path to SpecMatch-Emp library: ') # e.g., '/Users/abehmard/.specmatchemp/library.h5'
wavlim = [4990, 6094.9099] # wavelength range up to 12th HIRES order
lib = specmatchemp.library.read_hdf(lib_path,wavlim=wavlim)
# subset of SpecMatch-Emp library that suits user purposes (this version = cool stars)
cool = lib.library_params.query('Teff < 5200 and radius < 1')
print("There are "+str(len(lib.library_spectra[cool.lib_index,:,:]))+" targets in this training set")
bad_stars = [input('Enter names of targets to exclude: ')] # e.g., 'GL570B'
cool = cool[~cool['cps_name'].isin(bad_stars)]
print("Removing unwanted stars, there are now "+str(len(lib.library_spectra[cool.lib_index,:,:]))+" targets in this training set")
# normalizes training spectra via Gaussian filter
for ind in cool.lib_index:
lib.library_spectra[ind,0],lib.library_spectra[ind,1] =
gauss_norm(lib.library_spectra[ind,0],lib.wav,lib.library_spectra[ind,1],3)
# removes Na doublet - activity-driven feature
spectra_tr,errs_tr,wavs_tr = rm_Na_doublt(lib.library_spectra[cool.lib_index,0],lib.library_spectra[cool.lib_index,1],lib.wav)
# copies and broadens training set
augment=6 # broaden spectra copies up to augment-1 km/s
kernel_width=51 # ~ +/-25 km/s
cool_size=len(cool.lib_index)
broad_spectra_tr=[]
vsini=[]
for ind in range(cool_size):
for i in np.arange(0,augment,1):
broad_spectrum_tr = broaden_smsyn(spectra_tr[ind],kernel_width,1,0,i+1e-10)
broad_spectra_tr.append(broad_spectrum_tr)