def c60_example():
file1 = "/HD169454/RED_860/HD169454_w860_redu_20160808_O8.fits"
# file2 = "/HD54662/RED_860/HD54662_w860_redu_20160221_O8.fits"
xmin = 9555
xmax = 9600
sp1 = EdiblesSpectrum(file1)
sp1.getSpectrum(xmin=xmin, xmax=xmax)
sp1.flux = sp1.flux / np.max(sp1.flux)
plt.plot(sp1.wave, sp1.flux, "k")
plt.title("HD 169454", fontsize=14)
plt.xlabel(r"Wavelength ($\AA$)", fontsize=14)
plt.ylabel("Normalized Flux", fontsize=14)
plt.xticks(fontsize=12)
plt.yticks(fontsize=12)
plt.hlines(1, xmin=9574, xmax=9579, color='r')
plt.hlines(1, xmin=9578, xmax=9580, color='r', linestyle=':')
plt.text(9577.0, 1.01, r"C$_{60}^+$", fontsize=12)
plt.show()
def line_examples():
file = "/HD170740/RED_860/HD170740_w860_redl_20160613_O6.fits"
xmin = 7055
xmax = 7130
# GEOCENTRIC
sp1 = EdiblesSpectrum(file)
sp1.getSpectrum(xmin=xmin, xmax=xmax)
sp1.flux = sp1.flux / np.max(sp1.flux)
plt.plot(sp1.wave, sp1.flux, "k")
plt.title("HD 170740", fontsize=14)
plt.xlabel(r"Wavelength ($\AA$)", fontsize=14)
plt.ylabel("Normalized Flux", fontsize=14)
plt.xticks(fontsize=12)
plt.yticks(fontsize=12)
plt.vlines(7065, 0.9, 1, color='r')
plt.text(7065.2, 0.995, 'He I')
plt.vlines(7119.5, 0.9, 1, color='r')
plt.text(7119.7, 0.995, 'DIB')
plt.show()
if centwave == 860:
order_number = [21, 14]
wavemin = float(obs['WaveMin'])
wavemax = float(obs['WaveMax'])
spectrum_length = wavemax - wavemin
min_cutoff = spectrum_length * tr_min
max_cutoff = spectrum_length * tr_max
wavemin = wavemin + min_cutoff
wavemax = wavemax - max_cutoff
sp = EdiblesSpectrum(obs['Filename'])
idx = np.where(np.logical_and(sp.wave > wavemin, sp.wave < wavemax))
sp.wave = sp.wave[idx]
sp.flux = sp.flux[idx]
# build a 4 anchor points spline
cont = Continuum(sp, method="spline", n_anchors=4, plot=False, verbose=0)
# Guess the model parameters
params = cont.model.guess(sp.flux, x=sp.wave)
# Fit the model
result = cont.model.fit(data=sp.flux, params=params, x=sp.wave)
# Get the output of the fit model
out = result.eval(params=result.params, x=sp.wave)
resid = sp.flux - out
std = np.std(resid)
idx2 = np.where(sp.flux >= (out - std))
result2 = cont.model.fit(data=sp.flux[idx2], params=params, x=sp.wave[idx2])
def reference_frames():
file1 = "/HD170740/RED_860/HD170740_w860_redl_20140915_O12.fits"
file2 = "/HD170740/RED_860/HD170740_w860_redl_20140916_O12.fits"
file3 = "/HD170740/RED_860/HD170740_w860_redl_20150626_O12.fits"
file4 = "/HD170740/RED_860/HD170740_w860_redl_20160613_O12.fits"
file5 = "/HD170740/RED_860/HD170740_w860_redl_20170705_O12.fits"
xmin = 7661.5
xmax = 7669
# GEOCENTRIC
sp1 = EdiblesSpectrum(file1)
sp1.getSpectrum(xmin=xmin, xmax=xmax)
sp1.flux = sp1.flux / np.max(sp1.flux)
plt.plot(sp1.wave, sp1.flux, "k")
plt.text(7661.6, 0.8, sp1.datetime.date(), fontsize=12)
sp2 = EdiblesSpectrum(file2)
sp2.getSpectrum(xmin=xmin, xmax=xmax)
sp2.flux = sp2.flux / np.max(sp2.flux) + 1
plt.plot(sp2.wave, sp2.flux, "k")
plt.text(7661.6, 1.8, sp2.datetime.date(), fontsize=12)
sp3 = EdiblesSpectrum(file3)
sp3.getSpectrum(xmin=xmin, xmax=xmax)
sp3.flux = sp3.flux / np.max(sp3.flux) + 2
plt.plot(sp3.wave, sp3.flux, "k")
plt.text(7661.6, 2.8, sp3.datetime.date(), fontsize=12)
sp4 = EdiblesSpectrum(file4)
sp4.getSpectrum(xmin=xmin, xmax=xmax)
sp4.flux = sp4.flux / np.max(sp4.flux) + 3
plt.plot(sp4.wave, sp4.flux, "k")
plt.text(7661.6, 3.8, sp4.datetime.date(), fontsize=12)
sp5 = EdiblesSpectrum(file5)
sp5.getSpectrum(xmin=xmin, xmax=xmax)
sp5.flux = sp5.flux / np.max(sp5.flux) + 4
plt.plot(sp5.wave, sp5.flux, "k")
plt.text(7661.6, 4.8, sp5.datetime.date(), fontsize=12)
plt.title("HD 170740", fontsize=14)
plt.xlabel(r"Wavelength ($\AA$)", fontsize=14)
plt.ylabel("Normalized Flux + Offset", fontsize=14)
plt.xticks(fontsize=12)
plt.yticks(fontsize=12)
plt.show()
# BARYCENTRIC
sp1.bary_flux = sp1.bary_flux / np.max(sp1.bary_flux)
plt.plot(sp1.bary_wave, sp1.bary_flux, "k")
plt.text(7661.6, 0.8, sp1.datetime.date(), fontsize=12)
sp2.bary_flux = sp2.bary_flux / np.max(sp2.bary_flux) + 1
plt.plot(sp2.bary_wave, sp2.bary_flux, "k")
plt.text(7661.6, 1.8, sp2.datetime.date(), fontsize=12)
sp3.bary_flux = sp3.bary_flux / np.max(sp3.bary_flux) + 2
plt.plot(sp3.bary_wave, sp3.bary_flux, "k")
plt.text(7661.6, 2.8, sp3.datetime.date(), fontsize=12)
sp4.bary_flux = sp4.bary_flux / np.max(sp4.bary_flux) + 3
plt.plot(sp4.bary_wave, sp4.bary_flux, "k")
plt.text(7661.6, 3.8, sp4.datetime.date(), fontsize=12)
sp5.bary_flux = sp5.bary_flux / np.max(sp5.bary_flux) + 4
plt.plot(sp5.bary_wave, sp5.bary_flux, "k")
plt.text(7661.6, 4.8, sp5.datetime.date(), fontsize=12)
plt.title("HD 170740", fontsize=14)
plt.xlabel(r"Wavelength ($\AA$)", fontsize=14)
plt.ylabel("Normalized Flux + Offset", fontsize=14)
plt.xticks(fontsize=12)
plt.yticks(fontsize=12)
plt.show()