coadd = args.coadd
outlier_rejection = float(args.outlier_rejection)
modelset = str(args.modelset)
final_mcmc = args.final_mcmc
if final_mcmc:
save_to_path1 = save_to_path_base + '/init_mcmc'
save_to_path = save_to_path_base + '/final_mcmc'
else:
save_to_path = save_to_path_base + '/init_mcmc'
#####################################
data = nsp.Spectrum(name=sci_data_name,
order=order,
path=data_path,
applymask=applymask)
tell_data_name2 = tell_data_name + '_calibrated'
tell_sp = nsp.Spectrum(name=tell_data_name2,
order=data.order,
path=tell_path,
applymask=applymask)
data.updateWaveSol(tell_sp)
if coadd:
sci_data_name2 = str(args.coadd_sp_name)
if not os.path.exists(save_to_path):
os.makedirs(save_to_path)
data1 = copy.deepcopy(data)
outlier_rejection = float(args.outlier_rejection)
modelset = str(args.modelset)
instrument = str(args.instrument)
final_mcmc = args.final_mcmc
if final_mcmc:
save_to_path1 = save_to_path_base + '/init_mcmc'
save_to_path = save_to_path_base + '/final_mcmc'
else:
save_to_path = save_to_path_base + '/init_mcmc'
#####################################
data = nsp.Spectrum(name=sci_data_name,
path=data_path,
applymask=applymask,
instrument=instrument)
sci_data = data
"""
MCMC run for the science spectra. See the parameters in the makeModel function.
Parameters
----------
sci_data : sepctrum object
science data
tell_data : spectrum object
telluric data for calibrating the science spectra
def defringetelluric():
if not os.path.exists(save_to_path):
os.makedirs(save_to_path)
############################################
print(tell_data_name)
tell_sp = nspf.Spectrum(name=tell_data_name, order=order, path=tell_path)
clickpoints = []
def onclick(event):
print(event)
global clickpoints
clickpoints.append([event.xdata])
print(clickpoints)
plt.axvline(event.xdata, c='r', ls='--')
plt.draw()
if len(clickpoints) == 2:
print('Closing Figure')
plt.axvspan(clickpoints[0][0], clickpoints[1][0], color='0.5', alpha=0.5, zorder=-100)
plt.draw()
plt.pause(1)
plt.close('all')
### Draw the figure with the power spectrum
cal1 = tell_sp.flux#[pixel_range_start:pixel_range_end]
xdim = len(cal1)#[pixel_range_start:pixel_range_end])
nfil = xdim//2 + 1
# Smooth the continuum
cal1smooth = sp.ndimage.median_filter(cal1, size=30)
# Do the FFT
cal1fft = fftpack.rfft(cal1-cal1smooth)
yp = abs(cal1fft[0:nfil])**2
yp = yp / np.max(yp)
fig, ax1 = plt.subplots(figsize=(12,6))
cid = fig.canvas.mpl_connect('button_press_event', onclick)
freq = np.arange(nfil)
yp[0:3] = 0 # Fix for very low order noise
ax1.plot(freq, yp)
#ax1.axvline(f_high*2, c='r', ls='--')
#ax1.axvline(f_low*2, c='r', ls='--')
ax1.set_ylabel('Power Spectrum')
ax1.set_xlabel('1 / (1024 pix)')
ax1.set_title('Select the range you would like to filter out')
ax1.set_xlim(0, np.max(freq))
plt.show()
plt.close('all')
f_high = np.max(clickpoints)/2
f_low = np.min(clickpoints)/2
if method == 'wavelet':
#### Wavelets
from wavelets import WaveletAnalysis
xdim = len(tell_sp.flux)#[pixel_range_start:pixel_range_end])
cal1 = tell_sp.flux#[pixel_range_start:pixel_range_end]
# Smooth the continuum
smoothed = sp.ndimage.uniform_filter1d(cal1, 30)
splinefit = sp.interpolate.interp1d(np.arange(len(smoothed)), smoothed, kind='cubic')
cal1smooth = splinefit(np.arange(0, len(cal1))) #smoothed
# use wavelets package: WaveletAnalysis
enhance_row = cal1 - cal1smooth
#print(enhance_row)
dt = 0.1
wa = WaveletAnalysis(enhance_row, dt=dt, axis=0)
# wavelet power spectrum
power = wa.wavelet_power
# scales
scales = wa.scales
# associated time vector
t = wa.time
# reconstruction of the original data
rx = wa.reconstruction()
defringe_data = np.array(cal1.data, dtype=float)
# reconstruct the fringe image
#reconstruct_image = np.zeros(defringe_data.shape)
reconstruct_image = np.real(rx)
defringe_data -= reconstruct_image
newSpectrum = defringe_data
if PLOT:
fig = plt.figure(figsize=(12,6))
ax1 = plt.subplot2grid((3, 1), (0, 0))
ax2 = plt.subplot2grid((3, 1), (1, 0), rowspan=2)
#freq = np.arange(nfil)
ax1.plot(power**2)
ax1.axvline(f_high*2, c='r', ls='--')
ax1.axvline(f_low*2, c='r', ls='--')
ax1.set_ylabel('Power Spectrum')
ax1.set_xlabel('1 / (1024 pix)')
#ax1.set_xlim(0, np.max(freq))
ax2.plot(cal1[0:-23], label='original', alpha=0.5, lw=1, c='b')
ax2.plot(newSpectrum[0:-23]+0.5*np.median(newSpectrum[0:-23]), label='defringed', alpha=0.8, lw=1, c='r')
ax2.legend()
ax2.set_ylabel('Flux')
ax2.set_xlabel('Pixel')
ax2.set_xlim(0, len(cal1[0:-23]))
plt.tight_layout()
plt.savefig(save_to_path+"defringed_spectrum.png", bbox_inches='tight')
plt.show()
if method == 'hanningnotch':
## REDSPEC version
cal1 = tell_sp.flux#[pixel_range_start:pixel_range_end]
xdim = len(cal1)#[pixel_range_start:pixel_range_end])
nfil = xdim//2 + 1
#print(xdim, nfil//2+1)
freq = np.arange(nfil//2+1) / (nfil / float(xdim))
fil = np.zeros(len(freq), dtype=np.float)
fil[np.where((freq < f_low) | (freq > f_high))] = 1.
fil = np.append(fil, np.flip(fil[1:],axis=0))
fil = np.real(np.fft.ifft(fil))
fil = np.roll(fil, nfil//2)
fil = fil*np.hanning(nfil)
# Smooth the continuum
#smoothed = sp.ndimage.uniform_filter1d(cal1, 30)
#splinefit = sp.interpolate.interp1d(np.arange(len(smoothed)), smoothed, kind='cubic')
#cal1smooth = splinefit(np.arange(0, len(cal1))) #smoothed
cal1smooth = sp.ndimage.median_filter(cal1, size=30)
"""
plt.figure()
plt.plot(abs(np.real(fftpack.fft(cal1orig-cal1smooth)))**2, c='k', lw=0.5)
plt.plot(abs(np.real(fftpack.fft( sp.ndimage.convolve(cal1orig-cal1smooth, fil, mode='wrap') ) ))**2, c='r', lw=0.5)
plt.ylim(0,25000)
#plt.xlim(0,800)
plt.show()
#sys.exit()
plt.figure(figsize=(10,6))
plt.plot(cal1-cal1smooth, lw=0.5, c='k')
plt.plot(sp.ndimage.convolve(cal1-cal1smooth, fil, mode='wrap'), lw=0.5, c='r')
plt.plot(sp.ndimage.median_filter(cal1-cal1smooth, 10), lw=0.5, c='m')
plt.show(block=False)
#sys.exit()
plt.figure()
plt.plot( (cal1-cal1smooth)-sp.ndimage.convolve(cal1-cal1smooth, fil, mode='wrap'), lw=0.5, c='k')
plt.show(block=True)
#sys.exit()
"""
newSpectrum = sp.ndimage.convolve(cal1-cal1smooth, fil, mode='wrap') + cal1smooth
if PLOT:
# Do the FFT
cal1fft = fftpack.rfft(cal1-cal1smooth)
yp = abs(cal1fft[0:nfil])**2
yp = yp / np.max(yp)
yp[0:3] = 0 # Fix for very low order noise
fig = plt.figure(figsize=(12,6))
ax1 = plt.subplot2grid((3, 1), (0, 0))
ax2 = plt.subplot2grid((3, 1), (1, 0), rowspan=2)
freq = np.arange(nfil)
ax1.plot(freq, yp)
ax1.axvline(f_high*2, c='r', ls='--')
ax1.axvline(f_low*2, c='r', ls='--')
ax1.set_ylabel('Power Spectrum')
ax1.set_xlabel('1 / (1024 pix)')
ax1.set_xlim(0, np.max(freq))
ax2.plot(cal1[0:-23], label='original', alpha=0.5, lw=1, c='b')
ax2.plot(newSpectrum[0:-23]+0.5*np.median(newSpectrum[0:-23]), label='defringed', alpha=0.8, lw=1, c='r')
ax2.legend()
ax2.set_ylabel('Flux')
ax2.set_xlabel('Pixel')
ax2.set_xlim(0, len(cal1[0:-23]))
plt.tight_layout()
plt.savefig(save_to_path+"defringed_spectrum.png", bbox_inches='tight')
plt.show()
if method == 'flatfilter':
cal1 = tell_sp.flux#[pixel_range_start:pixel_range_end]
W = fftpack.fftfreq(cal1.size, d=1./1024)
fftval = fftpack.rfft(cal1.astype(float))
fftval[np.where((W > f_low) & (W < f_high))] = 0
newSpectrum = fftpack.irfft(fftval)
if PLOT:
xdim = len(cal1)#[pixel_range_start:pixel_range_end])
nfil = xdim//2 + 1
freq = np.arange(nfil)
# Smooth the continuum
smoothed = sp.ndimage.uniform_filter1d(cal1, 30)
splinefit = sp.interpolate.interp1d(np.arange(len(smoothed)), smoothed, kind='cubic')
cal1smooth = splinefit(np.arange(0, len(cal1))) #smoothed
# Do the FFT
cal1fft = fftpack.rfft(cal1-cal1smooth)
yp = abs(cal1fft[0:nfil])**2 # Power
yp = yp / np.max(yp)
fig = plt.figure(figsize=(12,6))
ax1 = plt.subplot2grid((3, 1), (0, 0))
ax2 = plt.subplot2grid((3, 1), (1, 0), rowspan=2)
ax1.plot(freq, yp)
ax1.axvline(f_high, c='r', ls='--')
ax1.axvline(f_low, c='r', ls='--')
ax1.set_ylabel('Power Spectrum')
ax1.set_xlabel('1 / (1024 pix)')
ax1.set_xlim(0, np.max(freq))
ax2.plot(cal1[0:-23], label='original', alpha=0.5, lw=1, c='b')
ax2.plot(newSpectrum[0:-23]+0.5*np.median(newSpectrum[0:-23]), label='defringed', alpha=0.8, lw=1, c='r')
ax2.legend()
ax2.set_ylabel('Flux')
ax2.set_xlabel('Pixel')
ax2.set_xlim(0, len(cal1[0:-23]))
plt.tight_layout()
plt.savefig(save_to_path+"defringed_spectrum.png", bbox_inches='tight')
#plt.show()
fullpath = tell_path + '/' + tell_data_name + '_' + str(order) + '_all.fits'
save_name = save_to_path + '%s_defringe_%s_all.fits'%(tell_data_name, order)
hdulist = fits.open(fullpath)
hdulist.append(fits.PrimaryHDU())
hdulist[-1].data = tell_sp.flux
hdulist[1].data = newSpectrum
hdulist[-1].header['COMMENT'] = 'Raw Extracted Spectrum'
hdulist[1].header['COMMENT'] = 'Defringed Spectrum'
try:
hdulist.writeto(save_name, overwrite=True)
except FileNotFoundError:
hdulist.writeto(save_name)
date_obs = str(args.date_obs[0]) tell_data_name = str(args.tell_data_name[0]) tell_path = str(args.tell_path[0]) save_to_path = str(args.save_to_path[0]) ndim, nwalkers, step = int(args.ndim), int(args.nwalkers), int(args.step) burn = int(args.burn) moves = float(args.moves) priors = args.priors applymask = args.applymask pixel_start, pixel_end = int(args.pixel_start), int(args.pixel_end) save = args.save if order == 35: applymask = True tell_data_name2 = tell_data_name + '_calibrated' tell_sp = nsp.Spectrum(name=tell_data_name2, order=order, path=tell_path, applymask=applymask) ########################################################################################################### """ MCMC routine for telluric standard stars to obtain the LSF and alpha. This function utilizes the emcee package. Parameters ---------- tell_sp : Spectrum object telluric spectrum nwalkers : int number of walkers. default is 30. step : int number of steps. default is 400 burn : int burn in mcmc to compute the best parameters. default is 300.