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 testBasicFit(filename="tests/HD170740_w860_redl_20140915_O12.fits"):
sp1 = EdiblesSpectrum(filename, noDATADIR=True)
xmin = 7661.5
xmax = 7669.0
subset = sp1.getSpectrum(xmin=xmin, xmax=xmax)
data = (subset["wave"], subset["flux"])
cont = createCont(data, n_points=4)
sightline = Sightline(star_name=sp1.target, cont=cont)
sightline.addSource(source_name="Telluric", b=0.001, d=0.05)
sightline.addLine(name="tell1", lam_0=7664.5, tau_0=0.6)
sightline.addLine(name="tell2", lam_0=7666, tau_0=0.6)
sightline.addSource(source_name="Interstellar", b=0.01, d=0.02)
sightline.addLine(name="int1", lam_0=7665.2, tau_0=0.2)
sightline.addLine(name="int2", lam_0=7665.3, tau_0=0.01)
fit_model = fit(sp1.target,
data,
sightline.model,
breakdown=False,
silent=True)
return fit_model
def testEdiblesSpectrum(filename="tests/HD170740_w860_redl_20140915_O12.fits"):
sp = EdiblesSpectrum(filename=filename, noDATADIR=True)
assert isinstance(sp.header, astropy.io.fits.header.Header)
assert isinstance(sp.target, str)
assert isinstance(sp.date, str)
assert isinstance(sp.v_bary, float)
assert isinstance(sp.df, pandas.core.frame.DataFrame)
assert isinstance(sp.wave, pandas.core.frame.Series)
assert isinstance(sp.wave_units, str)
assert isinstance(sp.bary_wave, pandas.core.frame.Series)
assert isinstance(sp.flux, pandas.core.frame.Series)
assert isinstance(sp.flux_units, str)
subset1 = sp.getSpectrum()
assert isinstance(subset1, pandas.core.frame.DataFrame)
assert isinstance(subset1.wave, pandas.core.frame.Series)
assert isinstance(subset1.flux, pandas.core.frame.Series)
assert sp.df.equals(subset1)
xmin = 7660
xmax = 7680
subset2 = sp.getSpectrum(xmin=xmin, xmax=xmax)
assert isinstance(subset2, pandas.core.frame.DataFrame)
assert isinstance(subset2.wave, pandas.core.frame.Series)
assert isinstance(subset2.flux, pandas.core.frame.Series)
assert np.min(subset2.wave) > xmin
assert np.max(subset2.wave) < xmax
def coadding():
file1 = "/HD170740/BLUE_437/HD170740_w437_blue_20140915_O15.fits"
file2 = "/HD170740/BLUE_437/HD170740_w437_blue_20140916_O15.fits"
file3 = "/HD170740/BLUE_437/HD170740_w437_blue_20150626_O15.fits"
file4 = "/HD170740/BLUE_437/HD170740_w437_blue_20160613_O15.fits"
file5 = "/HD170740/BLUE_437/HD170740_w437_blue_20170705_O15.fits"
xmin = 4220
xmax = 4227.5
sp1 = EdiblesSpectrum(file1)
sp2 = EdiblesSpectrum(file2)
sp3 = EdiblesSpectrum(file3)
sp4 = EdiblesSpectrum(file4)
sp5 = EdiblesSpectrum(file5)
observations = [sp1, sp2, sp3, sp4, sp5]
fig, axs = plt.subplots(1, 2)
shift = 0
for sp in observations:
shift += 0.01
sp.getSpectrum(xmin=xmin, xmax=xmax)
sp.interp_flux = sp.interp_flux / np.max(sp.interp_flux)
axs[0].plot(sp.grid, sp.interp_flux + shift)
superspectrum = np.ones_like(observations[0].interp_flux)
for sp in observations:
superspectrum = superspectrum * sp.interp_flux
axs[0].plot(observations[0].grid, superspectrum, 'k')
axs[0].set_xlabel(r"Wavelength ($\AA$)", fontsize=14)
axs[0].set_title("Geocentric Reference Frame", fontsize=14)
axs[0].tick_params(axis='both', labelsize=12)
# axs[0].set_yticklabels(fontsize=12)
shift = 0
for sp in observations:
shift += 0.01
sp.getSpectrum(xmin=xmin, xmax=xmax)
sp.interp_bary_flux = sp.interp_bary_flux / np.max(sp.interp_bary_flux)
axs[1].plot(sp.grid, sp.interp_bary_flux + shift)
superspectrum = np.ones_like(observations[0].interp_bary_flux)
for sp in observations:
superspectrum = superspectrum * sp.interp_bary_flux
axs[1].plot(observations[0].grid, superspectrum, 'k')
axs[1].set_xlabel(r"Wavelength ($\AA$)", fontsize=14)
axs[1].set_title("Barycentric Reference Frame", fontsize=14)
axs[1].tick_params(axis='both', labelsize=12)
# axs[0].set_yticklabels(fontsize=12)
plt.show()
def data():
file1 = "/HD170740/RED_860/HD170740_w860_redl_20160613_O12.fits"
sp1 = EdiblesSpectrum(file1)
sp1.getSpectrum(xmin=7585, xmax=7680)
plt.plot(sp1.wave, sp1.flux, "k")
plt.title("HD 170740", fontsize=14)
plt.xlabel(r"Wavelength ($\AA$)", fontsize=14)
plt.ylabel("Flux", fontsize=14)
plt.xticks(fontsize=12)
plt.yticks(fontsize=12)
plt.show()
def testAdvancedFit(
filename1="tests/HD170740_w860_redl_20140915_O12.fits",
filename2="tests/HD148937_w346_blue_20150817_O11.fits",
):
star_name = "HD170740"
xmin = 7661.0
xmax = 7670.0
sp = EdiblesSpectrum(filename1, noDATADIR=True)
subset = sp.getSpectrum(xmin, xmax)
data = (subset["wave"], subset["flux"])
# Cont parameters
n_points = 4
cont = createCont(data, n_points)
slightline = Sightline(star_name=star_name, cont=cont)
slightline.addSource(source_name="Telluric", b=1.07, d=0.046)
slightline.addLine(name="tell_1", lam_0=7664.8, tau_0=0.75)
slightline.addLine(name="tell_2", lam_0=7666, tau_0=0.75)
slightline.addSource(source_name="Interstellar", b=1.0, d=0.001)
slightline.addLine(name="KI_1", lam_0=7665.3, tau_0=0.1)
slightline.addLine(name="KI_2", lam_0=7665.35, tau_0=0.05)
fit_model = fit(star_name, data, slightline.model, silent=True)
star = "HD148937"
subset2 = EdiblesSpectrum(filename2, noDATADIR=True).getSpectrum(3301.5, 3304)
data2 = (subset2["wave"], subset2["flux"])
cont = createCont(data2, n_points=3)
slightline2 = Sightline(star_name=star, cont=cont)
slightline2.addSource("Source 1", 1.42631e-07, 0.036356)
slightline2.addLine("NaI_1", lam_0=3302.46, tau_0=0.06)
slightline2.addLine("NaI_2", lam_0=3303.1, tau_0=0.03)
slightline2.dupSource("Source 1", "Source 2", 1.00005)
slightline2.dupSource("Source 1", "Source 3", 0.99995)
fit_model2 = fit(star, data2, slightline2.model, silent=True)
return fit_model, fit_model2
def testModels(filename="tests/HD170740_w860_redl_20140915_O12.fits"):
method = 'least_squares'
sp = EdiblesSpectrum(filename, noDATADIR=True)
sp.getSpectrum(xmin=7661, xmax=7670)
n_anchors = 4
cont_model = ContinuumModel(n_anchors=n_anchors)
assert len(cont_model.param_names) == n_anchors * 2
assert cont_model.n_anchors == n_anchors
with pytest.raises(TypeError):
assert ContinuumModel()
with pytest.raises(TypeError):
assert ContinuumModel(n_anchors=11)
cont_pars = cont_model.guess(sp.flux, x=sp.wave)
assert len(cont_pars) == len(cont_model.param_names)
for name in cont_model.param_names:
assert cont_pars[name].value is not None
voigt = VoigtModel(prefix='voigt_')
assert voigt.prefix == 'voigt_'
assert len(voigt.param_names) == 4
voigt_pars = voigt.guess(sp.flux, x=sp.wave)
assert len(voigt_pars) == len(voigt.param_names)
result = voigt.fit(data=sp.flux,
params=voigt_pars,
x=sp.wave,
method=method)
assert len(result.params) == n_anchors
out = voigt.eval(data=sp.flux, params=result.params, x=sp.wave)
assert len(out) == len(sp.flux)
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()
import matplotlib.pyplot as plt
from edibles.sightline import Sightline
from edibles.utils.edibles_spectrum import EdiblesSpectrum
FILE1 = "/HD170740/RED_860/HD170740_w860_redl_20140915_O12.fits"
xmin = 7661.5
xmax = 7669
sp1 = EdiblesSpectrum(FILE1)
sp1.getSpectrum(xmin=7661, xmax=7670)
sightline = Sightline(sp1)
# Add source
sightline.add_source('telluric')
# Add line with auto-guessed params
sightline.add_line(name='line1', source='telluric')
# Add line with user defined params
d = {'d': 0.01, 'tau_0': 0.6, 'lam_0': 7664.8}
sightline.add_line(name='line2', pars=d, source='telluric')
# Add line with undefined source
d = {'d': 0.01, 'tau_0': 0.1, 'lam_0': 7665.2}
sightline.add_line(name='line3', source='interstellar')
# Add line with no source & user defined pars
d = {'d': 0.01, 'tau_0': 0.1, 'lam_0': 7662}
pars_list = convert(pars_list)
xmin = 7661.75
xmax = 7669
# Set tau_cutoff (0.4, 0.02, or 0.0)
tau_cutoff = 0.02
# Create linelist
linelist = []
for pars in pars_list:
if (xmin < pars["lam_0"]) and (pars["lam_0"] < xmax):
if pars["tau_0"] > tau_cutoff:
linelist.append(pars)
linelist.reverse()
print(linelist)
FILE1 = "/HD170740/RED_860/HD170740_w860_redl_20140915_O12.fits"
from edibles.utils.edibles_spectrum import EdiblesSpectrum
import matplotlib.pyplot as plt
sp1 = EdiblesSpectrum(FILE1)
sp1.getSpectrum(xmin=xmin, xmax=xmax)
plt.plot(sp1.wave, sp1.flux)
plt.vlines([line['lam_0'] for line in linelist], 0, 1000)
plt.show()
def CreateAverageSpectrum(DIB,
Target,
save_to_file=False,
save_figure=False,
verbose=True):
"""Create a DIB profile with an average spectrum for a sightline.
Args:
DIB (int):
Center wavelength of DIB.
Target (str):
Sightline of observation. Usually "HD {integer number}"
save_to_file (bool, optional):
If True, it saves the resulting profile. Defaults to False.
save_figure (bool, optional):
If True, it creates a plot of the resulting profile and saves it.
This plot contains the SN ratio. Defaults to False.
verbose (bool, optional):
If True, print the target date and resulting profile. Defaults to True.
Returns:
2darray:
Resulting average profile in the form of (wavelength, intensity).
"""
# Get Edibles files of that sightline.
oracle = EdiblesOracle()
List = oracle.getFilteredObsList([Target], Wave=DIB, MergedOnly=True)
# Dataframe to save the data.
df = pd.DataFrame()
# Offset for plotting
offset = 0.05
# Check if data is found.
if len(List) == 0:
print("Sightline files not found!")
return np.array([[0], [0]])
# Iterate over found datafiles.
for file in List:
# Get Edibles data.
sp = EdiblesSpectrum(file)
# Get target observation date and print it.
target_date = str(sp.datetime.date()).replace('-', '_')
if verbose:
print(target_date)
# Obtain data in wavelength range of interest.
sp.getSpectrum(xmin=DIB - 4, xmax=DIB + 4)
# Get wavelenth and flux
DIB_wavelength = np.asarray(sp.grid.byteswap().newbyteorder(),
dtype='float64')
DIB_flux = np.asarray(sp.interp_bary_flux.byteswap().newbyteorder(),
dtype='float64')
DIB_flux = DIB_flux / np.max(DIB_flux)
# Select datapoints to compute SN ratio.
cont_x1 = np.array(DIB_wavelength[-20:])
cont_y1 = np.array(DIB_flux[-20:])
cont_x2 = np.array(DIB_wavelength[:15])
cont_y2 = np.array(DIB_flux[:15])
# Get SN ratio
SN1, Fit1 = Signal_Noise_Calculator(cont_x1, cont_y1)
SN2, Fit2 = Signal_Noise_Calculator(cont_x2, cont_y2)
# Save data unvertainty
Signal_Noise = 0.5 * (SN1 + SN2)
New_Noise = DIB_flux / Signal_Noise
uncertainty = np.full(DIB_wavelength.shape,
np.mean(New_Noise),
dtype=float)
# Save results to dataframe
df[target_date + "_data"] = DIB_flux
df[target_date + "_error"] = uncertainty
# Add observation to plot.
if save_figure:
plt.plot(DIB_wavelength, DIB_flux + offset, label=target_date)
offset = offset + 0.05
# List to save stacking results.
weig_avg = []
weig_avg_err = []
# Print all the data obtained.
if verbose:
print(df)
# Compute weighted average.
for i in range(len(df.index)):
values = df[df.columns[::2]].iloc[i].values
error = df[df.columns[1::2]].iloc[i].values
avg_weig = weighted_average(values, error)
weig_avg.append(avg_weig[0])
weig_avg_err.append(avg_weig[1])
# Normalization
weig_avg = weig_avg / np.max(weig_avg)
# Save to dataframe
df["Weighted_Average"] = weig_avg
df["Weighted_Average_Error"] = weig_avg_err
# Add resulting profile to plot.
if save_figure:
plt.plot(DIB_wavelength, weig_avg, label='weighted_average')
plt.legend()
plt.savefig("AverageSpectra_" + str(DIB) + ".pdf")
plt.close()
# Save restul to file.
if save_to_file:
final = pd.DataFrame({
"Wavelength": DIB_wavelength,
"Flux": df["Weighted_Average"].to_numpy()
})
final.to_csv("Data/AverageSpectraData/" + str(DIB) + "/" + Target +
"_avg_spectra.csv")
# Return resulting profile.
return (DIB_wavelength, df["Weighted_Average"].to_numpy())
header = line1 + "\n" + line2
with open(csv_file, mode="a") as f:
np.savetxt(f, (x_points, y_points),
delimiter=",",
header=header,
comments="# ")
f.write("\n")
if self.verbose > 0:
print("Appended to file!")
if self.verbose > 1:
print("File appended to: " + csv_file)
if __name__ == "__main__":
sp = EdiblesSpectrum(
"/HD23466/BLUE_346/HD23466_w346_blue_20180731_O11.fits")
subset = sp.getSpectrum(xmin=3270, xmax=3305)
cont = Continuum(sp, method="spline", n_anchors=5, plot=True, verbose=2)
print("X names: ", cont.model.xnames)
print("X values: ",
[cont.result.params[param].value for param in cont.model.xnames])
print("Y names: ", cont.model.ynames)
print("Y values: ",
[cont.result.params[param].value for param in cont.model.ynames])
# cont.add_to_csv(user="******", comments="These are test points and should not be used.")
f.write("# n_anchors=" + str(self.model.n_anchors) + "\n")
f.write("# datetime=" + str(datetime.now()) + "\n")
f.write("# user="******"\n")
f.write("# comments=" + str(comments) + "\n")
np.savetxt(f, (x_points, y_points), delimiter=",")
f.write("\n")
if self.verbose > 0:
print("Appended to file!")
if self.verbose > 1:
print("File appended to: " + csv_file)
if __name__ == "__main__":
sp = EdiblesSpectrum("//HD164073/RED_564/HD164073_w564_redl_20180830_O22.fits")
# sp.getSpectrum(xmin=3270, xmax=3305)
# build a 4 anchor points spline
cont = Continuum(sp, method="spline", n_anchors=4, plot=False, verbose=2)
# 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)
# Print the result parameters
print(result.params)
# Plot
plt.plot(sp.wave, sp.flux)
plt.plot(sp.wave, out)
}
# %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
# %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
# IMPORT DATA
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 = 7640
xmax = 7680
sp1 = EdiblesSpectrum(file1)
sp2 = EdiblesSpectrum(file2)
sp3 = EdiblesSpectrum(file3)
sp4 = EdiblesSpectrum(file4)
sp5 = EdiblesSpectrum(file5)
observations = [sp1, sp2, sp3, sp4, sp5]
# %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
# TELLURIC WAVELENGTH CORRECTION
zoom_xmin = 7661.5
zoom_xmax = 7669
# Shift spectra
observations = telluric_shift(observations,
xmin=xmin,
if centwave == 437:
order_number = [32]
if centwave == 564:
order_number = [25, 17]
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
zorder=10,
label='Fit params')
plt.scatter(init_x,
init_y,
marker='x',
s=80,
color='k',
label='Initial params')
plt.legend()
plt.show()
# #################################################################################
# Example 2
# #################################################################################
sp = EdiblesSpectrum("/HD23466/BLUE_346/HD23466_w346_blue_20180731_O11.fits")
xmin = 3270
xmax = 3305
sp.getSpectrum(xmin=xmin, xmax=xmax)
cont_model = ContinuumModel(n_anchors=4)
cont_pars = cont_model.guess(sp.flux, x=sp.wave)
# ##############################
# Show initial model
out = cont_model.eval(data=sp.flux, params=cont_pars, x=sp.wave)
y_param_names = []
def testEdiblesSpectrum(filename="tests/HD170740_w860_redl_20140915_O12.fits"):
# Spectrum information
sp = EdiblesSpectrum(filename=filename, noDATADIR=True)
assert isinstance(sp.header, astropy.io.fits.header.Header)
assert isinstance(sp.target, str)
assert isinstance(sp.date, str)
assert isinstance(sp.datetime, datetime.datetime)
assert isinstance(sp.v_bary, float)
assert isinstance(sp.wave_units, str)
assert isinstance(sp.flux_units, str)
# Raw
assert isinstance(sp.raw_wave, np.ndarray)
assert isinstance(sp.raw_bary_wave, np.ndarray)
assert isinstance(sp.raw_flux, np.ndarray)
assert len(sp.raw_wave) == len(sp.raw_bary_wave)
assert len(sp.raw_wave) == len(sp.raw_flux)
assert isinstance(sp.raw_grid, np.ndarray)
assert len(sp.raw_grid) == 200443 # print(len(sp.raw_grid))
assert isinstance(sp.raw_sky_wave, np.ndarray)
assert isinstance(sp.raw_sky_flux, np.ndarray)
assert len(sp.raw_sky_wave) == len(sp.raw_sky_flux)
assert isinstance(sp.wave, np.ndarray)
assert isinstance(sp.bary_wave, np.ndarray)
assert isinstance(sp.flux, np.ndarray)
# getSpectrum
xmin = 7660
xmax = 7680
sp.getSpectrum(xmin=xmin, xmax=xmax)
assert xmin == sp.xmin
assert xmax == sp.xmax
assert isinstance(sp.wave, np.ndarray)
assert isinstance(sp.flux, np.ndarray)
assert len(sp.wave) == len(sp.flux)
assert np.min(sp.wave) > sp.xmin
assert np.max(sp.wave) < sp.xmax
assert isinstance(sp.bary_wave, np.ndarray)
assert isinstance(sp.bary_flux, np.ndarray)
assert len(sp.bary_wave) == len(sp.bary_flux)
assert np.min(sp.bary_wave) > sp.xmin
assert np.max(sp.bary_wave) < sp.xmax
assert isinstance(sp.grid, np.ndarray)
assert isinstance(sp.interp_flux, np.ndarray)
assert isinstance(sp.interp_bary_flux, np.ndarray)
assert len(sp.grid) == len(sp.interp_flux)
assert len(sp.grid) == len(sp.interp_bary_flux)
assert np.min(sp.grid) > sp.xmin
assert np.max(sp.grid) < sp.xmax
assert isinstance(sp.sky_wave, np.ndarray)
assert isinstance(sp.sky_flux, np.ndarray)
assert len(sp.sky_wave) == len(sp.sky_flux)
assert np.min(sp.sky_wave) > sp.xmin
assert np.max(sp.sky_wave) < sp.xmax
# shift
zoom_xmin = 7661
zoom_xmax = 7679
shift = 0.05
sp.shift(shift=shift, zoom_xmin=zoom_xmin, zoom_xmax=zoom_xmax)
assert isinstance(sp.wave, np.ndarray)
assert isinstance(sp.flux, np.ndarray)
assert len(sp.wave) == len(sp.flux)
assert np.min(sp.wave) > sp.xmin
assert np.max(sp.wave) < sp.xmax
assert isinstance(sp.bary_wave, np.ndarray)
assert isinstance(sp.bary_flux, np.ndarray)
assert len(sp.bary_wave) == len(sp.bary_flux)
assert np.min(sp.bary_wave) > sp.xmin
assert np.max(sp.bary_wave) < sp.xmax
assert isinstance(sp.grid, np.ndarray)
assert isinstance(sp.interp_flux, np.ndarray)
assert isinstance(sp.interp_bary_flux, np.ndarray)
assert len(sp.grid) == len(sp.interp_flux)
assert len(sp.grid) == len(sp.interp_bary_flux)
assert np.min(sp.grid) > sp.xmin
assert np.max(sp.grid) < sp.xmax
assert isinstance(sp.sky_wave, np.ndarray)
assert isinstance(sp.sky_flux, np.ndarray)
assert len(sp.sky_wave) == len(sp.sky_flux)
assert np.min(sp.sky_wave) > sp.xmin
assert np.max(sp.sky_wave) < sp.xmax
plt.scatter(peak_wavelengths, peak_fluxes, marker='x')
plt.show()
if __name__ == "__main__":
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 = 7669.0
sp1 = EdiblesSpectrum(file1)
sp1.getSpectrum(xmin, xmax)
sp2 = EdiblesSpectrum(file2)
sp2.getSpectrum(xmin, xmax)
sp3 = EdiblesSpectrum(file3)
sp3.getSpectrum(xmin, xmax)
sp4 = EdiblesSpectrum(file4)
sp4.getSpectrum(xmin, xmax)
sp5 = EdiblesSpectrum(file5)
sp5.getSpectrum(xmin, xmax)
observations = [sp1, sp2, sp3, sp4, sp5]
observations = correct(observations)
sightlines = []
for spec in observations:
# Do we have to filter out merged or single-order spectra? Note that if both
# MergedOnly and OrdersOnly are True, only the Merged spectra will be returned.
if MergedOnly and OrdersOnly:
print("ONLY RETURNING MERGED SPECTRA")
bool_order = self.obslog.Order != "Z"
if OrdersOnly is True:
bool_order = self.obslog.Order != "ALL"
if MergedOnly is True:
bool_order = self.obslog.Order == "ALL"
ind = np.where(bool_target_matches & bool_order)
# print(ind)
return self.obslog.iloc[ind].Filename
if __name__ == "__main__":
# print("Main")
pythia = EdiblesOracle()
List = pythia.getObsListByWavelength(5000, MergedOnly=True)
# print(List)
for filename in List:
sp = EdiblesSpectrum(filename)
plt.figure()
plt.title(filename)
plt.xlabel("Wavelength (" + r"$\AA$" + ")")
plt.xlim(5000, 5100)
plt.plot(sp.wave, sp.flux)
plt.show()
i_flux = f(i_wave)
spec.wave = i_wave
spec.flux = i_flux
return data
if __name__ == "__main__":
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"
sp1 = EdiblesSpectrum(FILE1)
subset1 = sp1.getSpectrum(xmin=7661.5, xmax=7669)
sp2 = EdiblesSpectrum(FILE2)
subset2 = sp2.getSpectrum(xmin=7661.5, xmax=7669)
sp3 = EdiblesSpectrum(FILE3)
subset3 = sp3.getSpectrum(xmin=7661.5, xmax=7669)
sp4 = EdiblesSpectrum(FILE4)
subset4 = sp4.getSpectrum(xmin=7661.5, xmax=7669)
sp5 = EdiblesSpectrum(FILE5)
subset5 = sp5.getSpectrum(xmin=7661.5, xmax=7669)
data = [sp1, sp2, sp3, sp4, sp5]
def telluric_shift_plots():
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 = 7640
xmax = 7680
zoom_xmin = 7661.5
zoom_xmax = 7669
sp1 = EdiblesSpectrum(file1)
sp2 = EdiblesSpectrum(file2)
sp3 = EdiblesSpectrum(file3)
sp4 = EdiblesSpectrum(file4)
sp5 = EdiblesSpectrum(file5)
observations = [sp1, sp2, sp3, sp4, sp5]
pars_list = convert(read_hitran("telluric_lines_HITRAN.txt",
molecule='O2'))
linevals = []
for pars in pars_list:
if (zoom_xmin < pars["lam_0"]) and (pars["lam_0"] < zoom_xmax):
if pars['tau_0'] > 0.4:
linevals.append(pars["lam_0"])
fig, axs = plt.subplots(1, 2)
for sp in observations:
sp.getSpectrum(xmin=zoom_xmin, xmax=zoom_xmax)
axs[0].plot(sp.wave,
sp.flux / np.max(sp.flux),
label=sp.datetime.date())
axs[0].scatter(linevals,
np.zeros_like(linevals),
c='k',
label='HITRAN data')
axs[0].legend()
axs[0].set_xlabel(r'Wavelength ($\AA$)', fontsize=14)
axs[0].set_ylabel('Normalized Flux', fontsize=14)
axs[0].tick_params(axis='both', labelsize=12)
sp1 = EdiblesSpectrum(file1)
sp2 = EdiblesSpectrum(file2)
sp3 = EdiblesSpectrum(file3)
sp4 = EdiblesSpectrum(file4)
sp5 = EdiblesSpectrum(file5)
observations = [sp1, sp2, sp3, sp4, sp5]
observations = telluric_shift(observations,
xmin=xmin,
xmax=xmax,
zoom_xmin=zoom_xmin,
zoom_xmax=zoom_xmax,
molecule='O2',
plot=False)
for sp in observations:
axs[1].plot(sp.wave,
sp.flux / np.max(sp.flux),
label=sp.datetime.date())
axs[1].scatter(linevals,
np.zeros_like(linevals),
c='k',
label='HITRAN data')
axs[1].legend()
axs[1].set_xlabel(r'Wavelength ($\AA$)', fontsize=14)
axs[1].set_ylabel('Normalized Flux', fontsize=14)
axs[1].tick_params(axis='both', labelsize=12)
plt.show()
def createObsList(dryrun=True):
"""Set up of the list of FITS files in the data directory with the necessary
information that we need to supply to the oracle to work. Essentially, we
will recursively list all of the FITS files first, then open each of them
to read the header and extract the information. Finally, store everything
in a text file.
:param dryrun: If True, dont save list. Default=True
:type drtyrun: bool
"""
print("Data Release in " + DATADIR)
allfitsfiles = []
for path, dirs, files in os.walk(DATADIR):
for file in files:
if file.endswith(".fits"):
fullfilename = os.path.join(path, file)
relative_filename = fullfilename[len(DATADIR):]
# print(relative_filename)
allfitsfiles.append(relative_filename)
print(len(allfitsfiles))
class obsfile:
def __init__(self):
self.filename = ""
self.object = ""
self.date_obs = ""
self.setting = ""
self.order = ""
self.wave_min = ""
self.wave_max = ""
self.ra = ""
self.dec = ""
self.exptime = ""
# self.order=0
# self.merged=False
n_files = len(allfitsfiles)
full_list = [obsfile() for i in range(n_files)]
for count in range(len(allfitsfiles)):
print(count, n_files, allfitsfiles[count])
full_list[count].filename = allfitsfiles[count]
# print(DATADIR + full_list[count].filename)
spec = EdiblesSpectrum(full_list[count].filename)
# print(spec.header)
full_list[count].object = spec.header["OBJECT"]
full_list[count].date_obs = spec.header["DATE-OBS"]
full_list[count].ra = spec.header["RA"]
full_list[count].dec = spec.header["DEC"]
full_list[count].exptime = spec.header["EXPTIME"]
idx_O = allfitsfiles[count].find("_O")
if idx_O != -1:
# print(idx)
idx_dot = allfitsfiles[count].find(".")
full_list[count].order = (allfitsfiles[count])[idx_O + 2: idx_dot]
else:
full_list[count].order = "ALL"
if "HIERARCH ESO INS GRAT1 WLEN" in spec.header:
full_list[count].setting = int(spec.header["HIERARCH ESO INS GRAT1 WLEN"])
if "HIERARCH ESO INS GRAT2 WLEN" in spec.header:
full_list[count].setting = int(spec.header["HIERARCH ESO INS GRAT2 WLEN"])
struct = spec.getSpectrum()
wave = struct["wave"]
full_list[count].wave_min = "{:.1f}".format(np.min(wave))
full_list[count].wave_max = "{:.1f}".format(np.max(wave))
del spec
# Create arrays of formatted strings to print to a csv file now.
pstrings = [
[
"Object",
"RA",
"DEC",
"DateObs",
"Setting",
"Order",
"WaveMin",
"WaveMax",
"Filename",
]
]
for count in range(n_files):
pstrings.append(
[
full_list[count].object,
full_list[count].ra,
full_list[count].dec,
full_list[count].date_obs,
full_list[count].setting,
full_list[count].order,
full_list[count].wave_min,
full_list[count].wave_max,
full_list[count].filename,
]
)
# Time to print things out! Let's use csv format to do that.
outfile = PYTHONDIR + "/edibles/data/" + DATARELEASE + "_ObsLog.csv"
# length_checker = np.vectorize(len)
# all_lengths = length_checker(allfitsfiles)
# print(np.max(all_lengths))
if dryrun is False:
with open(outfile, "w") as csvFile:
writer = csv.writer(csvFile)
writer.writerows(pstrings)
print('Wrote to file!')
return
for i, coef in enumerate(y_anchors[::1]):
pars['%sy_%i' % (self.prefix, i)].set(value=coef,
min=0,
max=2 * np.max(data))
return update_param_vals(pars, self.prefix, **kwargs)
if __name__ == "__main__":
import matplotlib.pyplot as plt
from edibles.utils.edibles_spectrum import EdiblesSpectrum
filename = "/HD170740/RED_860/HD170740_w860_redl_20140915_O12.fits"
sp = EdiblesSpectrum(filename)
print(sp.target)
sp.getSpectrum(xmin=7661, xmax=7670)
# sp.flux = sp.flux / np.median(sp.flux)
cont_model = ContinuumModel(n_anchors=4)
cont_pars = cont_model.guess(sp.flux, x=sp.wave)
result = cont_model.fit(data=sp.flux, params=cont_pars, x=sp.wave)
result.plot_fit()
plt.show()
voigt_model = VoigtModel()
voigt_pars = voigt_model.guess(sp.flux, x=sp.wave)
model = cont_model * voigt_model
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()
def fit_NaI_Lines(target, date):
"""A function to fit Na I 2S-2P doublet lines - still very basic
NaI1 = 3302.368 -> from edibles_linelist_atoms
NaI2 = 3302.978 -> from edibles_linelist_atoms
NaI_blue_diff = 0.61 -> lab data from https://www.pa.uky.edu/~peter/newpage/
NaI3 = 5889.951 -> from edibles_linelist_atoms
NaI4 = 5895.924 -> from edibles_linelist_atoms
NaI_red_diff = 5.975 -> lab data from https://www.pa.uky.edu/~peter/newpage/
blue_red_diff = 2587.583 -> NaI3 - NaI1
"""
wavelength = 3300
pythia = EdiblesOracle()
bluelist = pythia.GetObsListByWavelength(wavelength, OrdersOnly=True)
files = []
for filename in bluelist:
if target in filename:
if date in filename:
files.append(filename)
print(files)
sp = EdiblesSpectrum(files[0])
print(sp.target)
sp.getSpectrum(xmin=3300, xmax=3305)
sigma = np.std(sp.flux)
prominence = sigma
peaks, _ = find_peaks(-sp.flux, prominence=prominence)
peak_wavelengths = [sp.wave[i] for i in peaks]
# #########################################################################
cont = ContinuumModel(n_anchors=4)
cont_pars = cont.guess(sp.flux, x=sp.wave)
# #########################################################################
voigt1 = VoigtModel(prefix='v1_')
# voigt1_pars = voigt1.make_params(lam_0=3302, b=1, d=0.001, tau_0=0.01)
voigt1_pars = voigt1.guess(sp.flux, x=sp.wave)
# #########################################################################
voigt2 = VoigtModel(prefix='v2_')
voigt2_pars = voigt2.make_params(lam_0=peak_wavelengths[1],
b=1,
d=0.001,
tau_0=0.01)
voigt2_pars['v2_lam_0'].set(expr='v1_lam_0 + 0.61')
voigt2_pars['v2_b'].set(expr='v1_b')
# #########################################################################
model = cont * voigt1 * voigt2
pars = cont_pars + voigt1_pars + voigt2_pars
result = model.fit(data=sp.flux, params=pars, x=sp.wave)
# #########################################################################
result.params.pretty_print()
print('Ratio: ', result.params['v1_tau_0'] / result.params['v2_tau_0'])
print(result.fit_report())
plt.subplot(121)
result.plot_fit()
plt.title('Na I 3303')
print()
print()
# #########################################################################
# #########################################################################
wavelength = 5890
pythia = EdiblesOracle()
redlist = pythia.GetObsListByWavelength(wavelength, OrdersOnly=True)
files = []
for filename in redlist:
if target in filename:
if date in filename:
files.append(filename)
print(files)
sp = EdiblesSpectrum(files[1])
print(sp.target)
sp.getSpectrum(xmin=5885, xmax=5900)
# #########################################################################
cont = ContinuumModel(n_anchors=4)
cont_pars = cont.guess(sp.flux, x=sp.wave)
# #########################################################################
prominence = (np.max(sp.flux) - np.min(sp.flux)) * 0.5
peaks, _ = find_peaks(-sp.flux, prominence=prominence)
peak_wavelengths = [sp.wave[i] for i in peaks]
voigt3 = VoigtModel(prefix='v3_')
voigt3_pars = voigt3.make_params(lam_0=peak_wavelengths[0],
b=1,
d=0.001,
tau_0=0.4)
# voigt3_pars = voigt3.guess(sp.flux, x=sp.wave)
# #########################################################################
voigt4 = VoigtModel(prefix='v4_')
voigt4_pars = voigt4.make_params(lam_0=5896, b=1, d=0.001, tau_0=0.1)
voigt4_pars['v4_lam_0'].set(expr='v3_lam_0 + 5.975')
voigt4_pars['v4_b'].set(expr='v3_b')
# #########################################################################
model = cont * voigt3 * voigt4
pars = cont_pars + voigt3_pars + voigt4_pars
result = model.fit(data=sp.flux, params=pars, x=sp.wave)
# #########################################################################
result.params.pretty_print()
print('Ratio: ', result.params['v3_tau_0'] / result.params['v4_tau_0'])
print(result.fit_report())
plt.subplot(122)
result.plot_fit()
plt.title('Na I 5890')
plt.show()
# # ######################################################################### # # Find files # %%%%%%% # GUI!!!! # %%%%%%% # ######################################################################### # Create a spectrum from a fits file file = "/HD170740/RED_860/HD170740_w860_redl_20140915_O12.fits" print() sp = EdiblesSpectrum(file) # print(sp.target) # print(sp.date) # print(sp.v_bary) # print(sp.wave_units) # print(sp.flux_units) # print(sp.wave) # print(sp.bary_wave) # print(sp.flux) # ############################## # Plotting # plt.plot(sp.wave, sp.flux, label='Geocentric') # plt.plot(sp.bary_wave, sp.flux, label='Barycentric')
