snr_train = np.asarray(lib.library_params.snr[idx_train].values)
snr_val = np.asarray(lib.library_params.snr[idx_validate].values)
# ----------------------------------------------------------------------------------
# ----- create training set label matrix ------------------------------------------
# ----------------------------------------------------------------------------------
rstar = np.asarray(lib.library_params.radius[idx_train].values)
teff = np.asarray(lib.library_params.Teff[idx_train].values)
feh = np.asarray(lib.library_params.feh[idx_train].values)
vsini=[]
for spectrum in spectra_tr:
# assigns each star a vsini value determined from autocorrelation peak
vsini_star = autocorr(lib.wav,spectrum,0)
vsini.append(vsini_star)
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 = []
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)
# assigns each star a vsini value determined from autocorrelation peak
vsini_star = autocorr(wavs_tr,broad_spectrum_tr,i)
vsini.append(vsini_star)
print("assigned vsini")
vsini = np.asarray(vsini)
spectra_tr = np.asarray(broad_spectra_tr)
# constructs training set label matrix
teff = [lib.library_params.Teff[ind] for ind in cool.lib_index]
rstar = [lib.library_params.radius[ind] for ind in cool.lib_index]
feh = [lib.library_params.feh[ind] for ind in cool.lib_index]
labels_tr = np.vstack((teff,rstar,feh)).T
# augment label and spectral errs matrices to reflect broad spectra matrix
broad_labels_tr = [np.tile(labels_tr[i],(augment,1)) for i in range(len(labels_tr[:,0]))]
errs_tr = np.asarray([np.tile(errs_tr[i],(augment,1)) for i in range(len(labels_tr[:,0]))])