def test_statistics_gui_roi_spectrum(specviz_gui):
# Ensure that the test is run on an unmodified workspace instance
workspace = new_workspace(specviz_gui)
hub = Hub(workspace=workspace)
# Make region of interest cutout, using default cutout at .3 from the
# middle in either direction
specviz_gui.current_workspace.current_plot_window.plot_widget._on_add_linear_region(
)
# Simulate cutout for truth data
spectrum = extract_region(hub.plot_item._data_item.spectrum,
SpectralRegion(*hub.selected_region_bounds))
# pull out stats dictionary
stats_dict = specviz_gui.current_workspace._plugin_bars['Statistics'].stats
# Generate truth comparisons
truth_dict = {
'mean': spectrum.flux.mean(),
'median': np.median(spectrum.flux),
'stddev': spectrum.flux.std(),
'centroid': centroid(spectrum, region=None),
'snr': "N/A",
'fwhm': fwhm(spectrum),
'ew': equivalent_width(spectrum),
'total': line_flux(spectrum),
'maxval': spectrum.flux.max(),
'minval': spectrum.flux.min()
}
# compare!
assert stats_dict == truth_dict
workspace.close()
def test_statistics_gui_full_spectrum(specviz_gui):
# Ensure that the test is run on an unmodified workspace instance
workspace = new_workspace(specviz_gui)
hub = Hub(workspace=workspace)
# pull out stats dictionary
stats_dict = specviz_gui.current_workspace._plugin_bars['Statistics'].stats
# Generate truth comparisons
spectrum = hub.plot_item._data_item.spectrum
truth_dict = {
'mean': spectrum.flux.mean(),
'median': np.median(spectrum.flux),
'stddev': spectrum.flux.std(),
'centroid': centroid(spectrum, region=None),
'snr': "N/A",
'fwhm': fwhm(spectrum),
'ew': equivalent_width(spectrum),
'total': line_flux(spectrum),
'maxval': spectrum.flux.max(),
'minval': spectrum.flux.min()
}
# compare!
assert stats_dict == truth_dict
workspace.close()
def sky_centroids(sky_wave, smoothed_sky_flux, smoothed_sky_noise):
"""Returns centroids of skylines in the sky model.
Parameters
----------
sky_wave : tuple
Wavelength array
smoothed_sky_flux : tuple
Smoothed flux array
smoothed_sky_noise : tuple
Smoothed noise array
Returns
-------
sky_cents : tuple
Skyline centroids
"""
#Normalize sky model
sky_norm_wave, sky_norm_flux, sky_norm_noise = continuum_normalize(np.min(sky_wave), np.max(sky_wave), smoothed_sky_flux,
sky_wave, smoothed_sky_noise)
#Find centroids of skylines in model
centroids = [7843, 7916, 7995.5, 8347, 8467, 8829.5, 8922, 9378, 9442, 9794]
sky_bounds = [[7835, 7850], [7908, 7920], [7990, 8000], [8340, 8353], [8460, 8475], [8820, 8835], [8915, 8930],
[9370, 9385], [9435, 9447], [9785, 9799]]
sky = Spectrum1D(spectral_axis=sky_norm_wave*u.Angstrom, flux=sky_norm_flux*u.ct)
regions_sky = []
for i in range(len(sky_bounds)):
regions_sky.append(SpectralRegion(sky_bounds[i][0]*u.Angstrom, sky_bounds[i][-1]*u.Angstrom))
sky_cents = centroid(sky, regions_sky)
return sky_cents
def compute_stats(spectrum):
"""
Compute basic statistics for a spectral region.
Parameters
----------
spectrum : `~specutils.spectra.spectrum1d.Spectrum1D`
region: `~specutils.utils.SpectralRegion`
"""
flux = spectrum.flux
mean = flux.mean()
rms = np.sqrt(flux.dot(flux) / len(flux))
try:
snr_val = snr(spectrum)
except Exception as e:
snr_val = "N/A"
return {
'mean': mean,
'median': np.median(flux),
'stddev': flux.std(),
'centroid':
centroid(spectrum, region=None
), # we may want to adjust this for continuum subtraction
'rms': rms,
'snr': snr_val,
'fwhm': fwhm(spectrum),
'ew': equivalent_width(spectrum),
'total': line_flux(spectrum),
'maxval': flux.max(),
'minval': flux.min()
}
def test_statistics_gui_roi_spectrum(specviz_gui):
# Ensure that the test is run on an unmodified workspace instance
workspace = new_workspace(specviz_gui)
hub = Hub(workspace=workspace)
# Make region of interest cutout, using default cutout at .3 from the
# middle in either direction
specviz_gui.current_workspace.current_plot_window.plot_widget._on_add_linear_region()
# Simulate cutout for truth data
spectrum = extract_region(hub.plot_item._data_item.spectrum,
SpectralRegion(*hub.selected_region_bounds))
# pull out stats dictionary
stats_dict = specviz_gui.current_workspace._plugin_bars['Statistics'].stats
# Generate truth comparisons
truth_dict = {'mean': spectrum.flux.mean(),
'median': np.median(spectrum.flux),
'stddev': spectrum.flux.std(),
'centroid': centroid(spectrum, region=None),
'snr': "N/A",
'fwhm': fwhm(spectrum),
'ew': equivalent_width(spectrum),
'total': line_flux(spectrum),
'maxval': spectrum.flux.max(),
'minval': spectrum.flux.min()}
# compare!
assert stats_dict == truth_dict
workspace.close()
def test_statistics_gui_full_spectrum(specviz_gui):
# Ensure that the test is run on an unmodified workspace instance
workspace = new_workspace(specviz_gui)
hub = Hub(workspace=workspace)
# pull out stats dictionary
stats_dict = specviz_gui.current_workspace._plugin_bars['Statistics'].stats
# Generate truth comparisons
spectrum = hub.plot_item._data_item.spectrum
truth_dict = {'mean': spectrum.flux.mean(),
'median': np.median(spectrum.flux),
'stddev': spectrum.flux.std(),
'centroid': centroid(spectrum, region=None),
'snr': "N/A",
'fwhm': fwhm(spectrum),
'ew': equivalent_width(spectrum),
'total': line_flux(spectrum),
'maxval': spectrum.flux.max(),
'minval': spectrum.flux.min()}
# compare!
assert stats_dict == truth_dict
workspace.close()
def line(spec,wave1,wave2):
# finding the centorid and deriving guesses parameters
centre=centroid(spec, SpectralRegion(wave1*u.AA, wave2*u.AA))
centre=float(centre/(1. * u.AA))
FWHM=fwhm(spec)
FWHM=float(FWHM/(1. * u.AA))
A=line_flux(spec, SpectralRegion(lamb1*u.AA, lamb2*u.AA))
a=1* u.Unit('J cm-2 s-1 AA-1')
A=float(A/(1. * u.AA*a))
# PARAMETERS
return [centre,A,FWHM]
def compute_stats(spectrum):
"""
Compute basic statistics for a spectral region.
Parameters
----------
spectrum : `~specutils.spectra.spectrum1d.Spectrum1D`
region: `~specutils.utils.SpectralRegion`
"""
try:
cent = centroid(spectrum, region=None) # we may want to adjust this for continuum subtraction
except Exception as e:
logging.debug(e)
cent = "Error"
try:
snr_val = snr(spectrum)
except Exception as e:
logging.debug(e)
snr_val = "N/A"
try:
fwhm_val = fwhm(spectrum)
except Exception as e:
logging.debug(e)
fwhm_val = "Error"
try:
ew = equivalent_width(spectrum)
except Exception as e:
logging.debug(e)
ew = "Error"
try:
total = line_flux(spectrum)
except Exception as e:
logging.debug(e)
total = "Error"
return {'mean': spectrum.flux.mean(),
'median': np.median(spectrum.flux),
'stddev': spectrum.flux.std(),
'centroid': cent,
'snr': snr_val,
'fwhm': fwhm_val,
'ew': ew,
'total': total,
'maxval': spectrum.flux.max(),
'minval': spectrum.flux.min()}
def get_toi837_li_equivalent_width():
spectrum_path = '../data/spectra/TOI-837_FEROS.fits'
plotpath = '../results/TOI_837/feros_spectrum_get_li_equivalent_width.png'
#
# fit out the continuum to get the continuum normalized flux, over the
# window of 6670 angstrom to 6713 angstrom.
#
xlim = [6670, 6713]
hdul = fits.open(spectrum_path)
d = hdul[0].data
wav = d[0, 0]
flx = d[3, 0]
if isinstance(xlim, list):
xmin = xlim[0]
xmax = xlim[1]
sel = (wav > xmin) & (wav < xmax)
wav = wav[sel]
flx = flx[sel]
spec = Spectrum1D(spectral_axis=wav * u.AA,
flux=flx * u.dimensionless_unscaled)
if isinstance(xlim, list):
exclude_regions = []
if xmin < 6709.2 and xmax > 6710.2:
exclude_regions.append(SpectralRegion(6709.2 * u.AA,
6710.2 * u.AA))
if xmin < 6679 and xmax > 6681:
exclude_regions.append(SpectralRegion(6679 * u.AA, 6681 * u.AA))
cont_flx = (fit_generic_continuum(spec, exclude_regions=exclude_regions)(
spec.spectral_axis))
cont_norm_spec = spec / cont_flx
#
# to fit gaussians, look at 1-flux.
#
full_spec = Spectrum1D(spectral_axis=cont_norm_spec.wavelength,
flux=(1 - cont_norm_spec.flux))
#
# get the Li EW
#
region = SpectralRegion(6708.5 * u.AA, 6711.5 * u.AA)
li_equiv_width = equivalent_width(cont_norm_spec, regions=region)
li_centroid = centroid(full_spec, region)
#
# fit a gaussian too, and get ITS equiv width
# https://specutils.readthedocs.io/en/stable/fitting.html
#
g_init = models.Gaussian1D(amplitude=0.2 * u.dimensionless_unscaled,
mean=6709.7 * u.AA,
stddev=0.5 * u.AA)
g_fit = fit_lines(full_spec, g_init, window=(region.lower, region.upper))
y_fit = g_fit(full_spec.wavelength)
fitted_spec = Spectrum1D(spectral_axis=full_spec.wavelength,
flux=(1 - y_fit) * u.dimensionless_unscaled)
fitted_li_equiv_width = equivalent_width(fitted_spec, regions=region)
#
# print bestfit params
#
print(42 * '=')
print('got Li equiv width of {}'.format(li_equiv_width))
print('got fitted Li equiv width of {}'.format(fitted_li_equiv_width))
print('got Li centroid of {}'.format(li_centroid))
print('fit gaussian1d params are\n{}'.format(repr(g_fit)))
print(42 * '=')
#
# plot the results
#
f, axs = plt.subplots(nrows=4, ncols=1, figsize=(6, 8))
axs[0].plot(wav, flx, c='k', zorder=3)
axs[0].plot(wav, cont_flx, c='r', zorder=2)
axs[1].plot(cont_norm_spec.wavelength, cont_norm_spec.flux, c='k')
axs[2].plot(cont_norm_spec.wavelength, cont_norm_spec.flux, c='k')
axs[3].plot(full_spec.wavelength, full_spec.flux, c='k')
axs[3].plot(full_spec.wavelength, y_fit, c='g')
txt = ('gaussian1d\namplitude:{:.3f}\nmean:{:.3f}\nstd:{:.3f}\nEW:{:.3f}'.
format(g_fit.amplitude.value, g_fit.mean.value, g_fit.stddev.value,
fitted_li_equiv_width))
axs[3].text(0.95,
0.95,
txt,
ha='right',
va='top',
transform=axs[3].transAxes,
fontsize='xx-small')
axs[0].set_ylabel('flux')
axs[1].set_ylabel('contnorm flux')
axs[2].set_ylabel('contnorm flux [zoom]')
axs[3].set_ylabel('1 - (contnorm flux)')
if isinstance(xlim, list):
for ax in axs:
ax.set_xlim(xlim)
axs[2].set_xlim([6708.5, 6711.5])
axs[3].set_xlim([6708.5, 6711.5])
axs[-1].set_xlabel('wavelength [angstrom]')
for ax in axs:
format_ax(ax)
outpath = '../results/TOI_837/toi837_li_equivalent_width_routine.png'
savefig(f, outpath)