pypeit.core.flux_calib module

Module for fluxing routines

pypeit.core.flux_calib.Flam_to_Nlam(wave, zeropoint, zp_min=5.0, zp_max=30.0)[source]

The factor that when multiplied into F_lam converts to N_lam, i.e. 1/S_lam where S_lam equiv F_lam/N_lam

Parameters:

  • wave (numpy.ndarray) – Wavelength array, float, shape (nspec,)

  • zeropoint (numpy.ndarray) – zeropoint array, float, shape (nspec,)

Returns:

factor – Factor that when multiplied into F_lam converts to N_lam, i.e. 1/S_lam

Return type:

numpy.ndarray

pypeit.core.flux_calib.Nlam_to_Flam(wave, zeropoint, zp_min=5.0, zp_max=30.0)[source]

The factor that when multiplied into N_lam converts to F_lam, i.e. S_lam where S_lam equiv F_lam/N_lam

Parameters:

  • wave (numpy.ndarray) – Wavelength vector for zeropoint

  • zeropoint (numpy.ndarray) – zeropoint

  • zp_min (float, optional) – Minimum allowed value of the ZP. For smaller values the S_lam factor is set to zero

  • zp_max (float, optional) – Maximum allowed value of the ZP. For larger values the S_lam factor is set to zero

Returns:

factor – S_lam factor

Return type:

numpy.ndarray

pypeit.core.flux_calib.compute_zeropoint(wave, N_lam, N_lam_gpm, flam_std_star, tellmodel=None)[source]

Routine to compute the zeropoint and zeropoint_gpm from the N_lam (counts/s/A) of a standard star

Parameters:

  • wave (numpy.ndarray) – Wavelength array, float, shape (nspec,)

  • N_lam (numpy.ndarray) – N_lam spectrum of standard star, float, shape (nspec,)

  • N_lam_gpm (numpy.ndarray) – N_lam mask, good pixel mask, boolean, shape (nspec,)

  • flam_std_star (numpy.ndarray) – True standard star spectrum in units of PYPEIT_FLUX_SCALE erg/s/cm^2/Angstrom

  • tellmodel (numpy.ndarray) – Telluric absorption model, optional, shape (nspec,)

Returns:

  • zeropoint (numpy.ndarray) – Spectroscopic zeropoint, float, shape (nspec,)

  • zeropoint_gpm (numpy.ndarray) – Zeropoint good pixel mask, bool, shape (nspec,)

pypeit.core.flux_calib.counts2Nlam(wave, counts, counts_ivar, counts_mask, exptime, airmass, atmext)[source]

Convert counts to counts/s/Angstrom Used for flux calibration and to apply extinction correction

Parameters:

  • wave (numpy.ndarray) – Wavelength of the star. Shape (nspec,)

  • counts (numpy.ndarray) – Flux (in counts) of the star. Shape (nspec,)

  • counts_ivar (numpy.ndarray) – Inverse variance of the star counts. Shape (nspec,)

  • counts_mask (numpy.ndarray) – Good pixel mask for the counts.

  • exptime (float) – Exposure time in seconds

  • airmass (float) – Airmass

  • atmext (AtmosphericExtinction) – Class that provides the interface to the atmospheric extinction data.

Returns:

Three items:

Return type:

tuple

pypeit.core.flux_calib.eval_zeropoint(theta, func, wave, wave_min, wave_max, log10_blaze_func_per_ang=None)[source]

Evaluate the zeropoint model.

Parameters:

  • theta (numpy.ndarray) – Parameter vector for the zeropoint model

  • func (callable) – Function for the zeropoint model from the set of available functions in evaluate_fit().

  • wave (numpy.ndarray, shape = (nspec,)) – Wavelength vector for zeropoint.

  • wave_min (float) – Minimum wavelength for the zeropoint fit to be passed as an argument to evaluate_fit()

  • wave_max (float) – Maximum wavelength for the zeropoint fit to be passed as an argument to evaluate_fit()

  • log10_blaze_func_per_ang (numpy.ndarray, optional, shape = (nspec,)) – Log10 blaze function per angstrom. This option is used if the zeropoint model is relative to the non-parametric blaze function determined from flats. The blaze function is defined on the wavelength grid wave.

Returns:

zeropoint – Zeropoint evaluated on the wavelength grid wave.

Return type:

numpy.ndarray, shape = (nspec,)

pypeit.core.flux_calib.find_standard(specobj_list)[source]

Routine to identify the standard star given a list of spectra

Take the median boxcar and then take the max flux object (in BOX_COUNTS) as the standard

Parameters:

specobj_list (list) – pypeit.specobj.SpecObj list

Returns:

mxix – Index of the standard star in the list

Return type:

int

pypeit.core.flux_calib.fit_zeropoint(wave, Nlam_star, Nlam_ivar_star, gpm_star, std_spec, mask_hydrogen_lines=True, mask_helium_lines=False, polyorder=4, hydrogen_mask_wid=10.0, nresln=20.0, resolution=3000.0, trans_thresh=0.9, polycorrect=True, polyfunc=False, debug=False)[source]

Function to generate the sensitivity function. This function fits a bspline to the 2.5*log10(flux_std/flux_counts). The break points spacing, which determines the scale of variation of the sensitivity function is determined by the nresln parameter.

Parameters:

  • wave (numpy.ndarray) – Wavelength of the star. Shape (nspec,)

  • Nlam_star (numpy.ndarray) – counts/second/Angstrom

  • Nlam_ivar_star (numpy.ndarray) – Inverse variance of Nlam_star

  • gpm_star (numpy.ndarray) – Good pixel mask for Nlam_star

  • std_spec (Spectrum) – Spectrum of the flux-calibration standard.

  • mask_hydrogen_lines (bool, optional) – If True, mask stellar hydrogen absorption lines before fitting sensitivity function. Default = True

  • mask_helium_lines (bool, optional) – If True, mask stellar helium absorption lines before fitting sensitivity function. Default = False

  • hydrogen_mask_wid (float, optional) – Parameter describing the width of the mask for or stellar absorption lines (i.e., mask_hydrogen_lines=True) in Angstroms. A region equal to hydrogen_mask_wid on either side of the line center is masked. Default = 10A

  • polycorrect (bool, optional) – Whether you want to interpolate the zeropoint with polynomial in the stellar absortion line regions before fitting with the bspline

  • nresln (float, optional) – Parameter governing the spacing of the bspline breakpoints. default = 20.0

  • resolution (float, optional) – Expected resolution of the standard star spectrum. This should probably be determined from the grating, but is currently hard wired. default=3000.0

  • trans_thresh (float, optional) – Parameter for selecting telluric regions which are masked. Locations below this transmission value are masked. If you have significant telluric absorption you should be using telluric.sensnfunc_telluric. default = 0.9

  • polyfunc (bool, optional) – If True, the zeropoint was a polynomial and not a bspline

Returns:

  • zeropoint_data (numpy.ndarray) – Sensitivity function with same shape as wave (nspec,)

  • zeropoint_data_gpm (numpy.ndarray) – Good pixel mask for sensitivity function with same shape as wave (nspec,)

  • zeropoint_fit (numpy.ndarray) – Fitted sensitivity function with same shape as wave (nspec,)

  • zeropoint_fit_gpm (numpy.ndarray) – Good pixel mask for fitted sensitivity function with same shape as wave (nspec,)

Return type:

tuple

pypeit.core.flux_calib.get_mask(wave_star, flux_star, ivar_star, mask_star, mask_hydrogen_lines=True, mask_helium_lines=False, mask_telluric=True, hydrogen_mask_wid=10.0, trans_thresh=0.9)[source]

Generate a set of masks from your observed standard spectrum. e.g. Balmer absorption

Parameters:

  • wave_star (numpy.ndarray) – wavelength array of your spectrum

  • flux_star (numpy.ndarray) – flux array of your spectrum

  • ivar_star (numpy.ndarray) – ivar array of your spectrum

  • mask_star (bool, optional) – whether you need to mask Hydrogen recombination line region. If False, the returned msk_star are all good.

  • mask_hydrogen_lines (bool, optional) – whether you need to mask hydrogen absorption lines, mask width set by hydrogen_mask_wid

  • mask_helium_lines (bool, optional) – whether you need to mask hydrogen absorption lines, mask width set to \(0.5 \times\) hydrogen_mask_wid

  • mask_telluric (bool, optional) – whether you need to mask telluric region. If False, the returned msk_tell are all good.

  • hydrogen_mask_wid (float, optional) – in units of angstrom Mask parameter for hydrogen recombination absorption lines. A region equal to hydrogen_mask_wid on either side of the line center is masked.

  • trans_thresh (float, optional) – parameter for selecting telluric regions.

Returns:

  • gpm_star (bool numpy.ndarray) – mask for good pixels (True = good pixel).

  • mask_recomb (bool numpy.ndarray) – mask for recombination lines in star spectrum.

  • mask_tell (bool numpy.ndarray) – mask for telluric regions.

pypeit.core.flux_calib.get_sensfunc_factor(wave, wave_zp, zeropoint, exptime, tellmodel=None, delta_wave=None, atmext=None, airmass=None, extrap_sens=False)[source]

Get the final sensitivity function factor that will be multiplied into a spectrum in units of counts to flux calibrate it. This code interpolates the sensitivity function and can also multiply in extinction and telluric corrections.

FLAM, FLAM_SIG, and FLAM_IVAR are generated

Parameters:

  • wave (float numpy.ndarray) – shape = (nspec,) Wavelength vector for the spectrum to be flux calibrated

  • wave_zp (float numpy.ndarray) – Zeropoint wavelength vector shape = (nsens,)

  • zeropoint (float numpy.ndarray) – shape = (nsens,) Zeropoint, i.e. sensitivity function

  • exptime (float) – Exposure time in seconds

  • tellmodel (float numpy.ndarray, optional) – Apply telluric correction if it is passed it (shape = (nspec,)). Note this is only used to generate the std fluxed QA plot. It should be None otherwise. To telluric correct the data, use the telluric correct method.

  • delta_wave (float, numpy.ndarray, optional) – The wavelength sampling of the spectrum to be flux calibrated.

  • atmext (AtmosphericExtinction, optional) – Class that provides the interface to the atmospheric extinction data.

  • airmass (float, optional) – Airmass used if extinct_correct=True. This is required if extinct_correct=True

  • extrap_sens (bool, optional) – Extrapolate the sensitivity function (instead of crashing out)

Returns:

sensfunc_factor – This quantity is defined to be sensfunc_interp/exptime/delta_wave. shape = (nspec,)

Return type:

numpy.ndarray

pypeit.core.flux_calib.load_filter_file(filter)[source]

Load a system response curve for a given filter. All supported filters can be found at pypeit.data.filters

Parameters:

filter (str) – Name of filter

Returns:

pypeit.core.flux_calib.mask_stellar_helium(wave_star, mask_width=5.0, mask_star=None)[source]

Routine to mask stellar helium recombination lines

Note

This function is pulled out separate from get_mask() because it is used in the telluric module, independent of the remainder of the functionality in get_mask().

Parameters:

  • wave_star (numpy.ndarray) – Wavelength of the stellar spectrum shape (nspec,) or (nspec, nimgs)

  • mask_width (float, optional) – width to mask on either side of each line center in Angstroms

  • mask_star (numpy.ndarray, optional) – Incoming star mask to which to add the ionized helium lines

Returns:

boolean mask. Same shape as wave_star, True=Good (i.e. does not hit a stellar absorption line).

Return type:

numpy.ndarray

pypeit.core.flux_calib.mask_stellar_hydrogen(wave_star, mask_width=10.0, mask_star=None)[source]

Routine to mask stellar hydrogen recombination lines

Note

This function is pulled out separate from get_mask() because it is used in the telluric module, independent of the remainder of the functionality in get_mask().

Parameters:

  • wave_star (numpy.ndarray) – Wavelength of the stellar spectrum shape (nspec,) or (nspec, nimgs)

  • mask_width (float, optional) – width to mask on either side of each line center in Angstroms

  • mask_star (numpy.ndarray, optional) – Incoming star mask to which to add the hydrogen lines

Returns:

boolean mask. Same shape as wave_star, True=Good (i.e. does not hit a stellar absorption line).

Return type:

numpy.ndarray

pypeit.core.flux_calib.scale_in_filter(wave, flux, gpm, scale_dict)[source]

Scale spectra to input magnitude in a given filter

Parameters:
Returns:

scale – scale value for the flux, i.e. newflux = flux * scale

Return type:

float

pypeit.core.flux_calib.sensfunc(wave, counts, counts_ivar, counts_mask, exptime, airmass, std_spec, atmext, ech_orders=None, mask_hydrogen_lines=True, mask_helium_lines=False, polyorder=4, hydrogen_mask_wid=10.0, nresln=20.0, resolution=3000.0, trans_thresh=0.9, polycorrect=True, polyfunc=False, debug=False)[source]

Function to generate the sensitivity function. This function fits a bspline to the 2.5*log10(flux_std/flux_counts). The break points spacing, which determines the scale of variation of the sensitivity function is determined by the nresln parameter. This code can work in different regimes, but NOTE THAT TELLURIC MODE IS DEPRECATED, use telluric.sensfunc_telluric instead

Parameters:

  • wave (numpy.ndarray) – Wavelength of the star. Shape (nspec,) or (nspec, norders)

  • counts (numpy.ndarray) – Flux (in counts) of the star. Shape (nspec,) or (nspec, norders)

  • counts_ivar (numpy.ndarray) – Inverse variance of the star counts. Shape (nspec,) or (nspec, norders)

  • counts_mask (numpy.ndarray) – Good pixel mask for the counts. Shape (nspec,) or (nspec, norders)

  • exptime (float) – Exposure time in seconds

  • airmass (float) – Airmass

  • std_spec (Spectrum) – Spectrum of the flux-calibration standard.

  • atmext (AtmosphericExtinction) – Class that provides the interface to the atmospheric extinction data.

  • ech_orders (int numpy.ndarray) – If passed the echelle orders will be added to the meta_table. ech_orders must be a numpy array of integers with the shape (norders,) giving the order numbers

  • mask_hydrogen_lines (bool) – If True, mask stellar hydrogen absorption lines before fitting sensitivity function. Default = True

  • mask_helium_lines (bool) – If True, mask stellar helium absorption lines before fitting sensitivity function. Default = False

  • balm_mask_wid (float) – Parameter describing the width of the mask for or stellar absorption lines (i.e. mask_hydrogen_lines=True). A region equal to balm_mask_wid*resln is masked where resln is the estimate for the spectral resolution in pixels per resolution element.

  • polycorrect (bool) – Whether you want to interpolate the sensfunc with polynomial in the stellar absortion line regions before fitting with the bspline

  • nresln (float) – Parameter governing the spacing of the bspline breakpoints. default = 20.0

  • resolution (float) – Expected resolution of the standard star spectrum. This should probably be determined from the grating, but is currently hard wired. default=3000.0

  • trans_thresh (float) – Parameter for selecting telluric regions which are masked. Locations below this transmission value are masked. If you have significant telluric absorption you should be using telluric.sensnfunc_telluric. default = 0.9

Returns:

Returns the following: - meta_table: astropy.table.Table Table containing meta data for the sensitivity function - out_table: astropy.table.Table Table containing the sensitivity function

Return type:

tuple

pypeit.core.flux_calib.standard_zeropoint(wave, Nlam, Nlam_ivar, Nlam_gpm, flam_true, mask_recomb=None, mask_tell=None, maxiter=35, upper=3.0, lower=3.0, func='polynomial', polyorder=5, balm_mask_wid=50.0, nresln=20.0, resolution=2700.0, polycorrect=True, debug=False, polyfunc=False)[source]

Generate a sensitivity function based on observed flux and standard spectrum.

Parameters:

  • wave (numpy.ndarray) – wavelength as observed

  • Nlam (numpy.ndarray) – counts/s/Angstrom as observed

  • Nlam_ivar (numpy.ndarray) – inverse variance of counts/s/Angstrom

  • Nlam_gpm (numpy.ndarray) – mask for bad pixels. True is good.

  • flam_true (astropy.units.Quantity) – array with true standard star flux (erg/s/cm^2/A)

  • mask_recomb (numpy.ndarray) – mask for hydrogen (and/or helium II) recombination lines. True is good.

  • mask_tell (numpy.ndarray) – mask for telluric regions. True is good.

  • maxiter (int) – maximum number of iterations for polynomial fit

  • upper (int) – number of sigma for rejection in polynomial

  • lower (int) – number of sigma for rejection in polynomial

  • polyorder (int) – order of polynomial fit

  • balm_mask_wid (float) – Mask parameter for Balmer absorption. A region equal to balm_mask_wid in units of angstrom is masked.

  • nresln (int, float) – number of resolution elements between breakpoints

  • resolution (int, float) – The spectral resolution. This paramters should be removed in the future. The resolution should be estimated from spectra directly.

  • debug (bool) – if True shows some dubugging plots

Returns:

  • zeropoint_data (numpy.ndarray) – Sensitivity function with same shape as wave (nspec,)

  • zeropoint_data_gpm (numpy.ndarray) – Good pixel mask for sensitivity function with same shape as wave (nspec,)

  • zeropoint_fit (numpy.ndarray) – Fitted sensitivity function with same shape as wave (nspec,)

  • zeropoint_fit_gpm (numpy.ndarray) – Good pixel mask for fitted sensitivity function with same shape as wave (nspec,)

pypeit.core.flux_calib.zeropoint_qa_plot(wave, zeropoint_data, zeropoint_data_gpm, zeropoint_fit, zeropoint_fit_gpm, title='Zeropoint QA', axis=None, show=False)[source]

QA plot for zeropoint

Parameters:

  • wave (numpy.ndarray) – Wavelength array

  • zeropoint_data (numpy.ndarray) – Zeropoint data array

  • zeropoint_data_gpm (boolean numpy.ndarray) – Good pixel mask array for zeropoint_data

  • zeropoint_fit (numpy.ndarray) – Zeropoint fitting array

  • zeropoint_fit_gpm (boolean numpy.ndarray) – Good pixel mask array for zeropoint_fit

  • title (str, optional) – Title for the QA plot

  • axis (matplotlib.axes.Axes, optional) – axis used for ploting. If None, a new plot is created

  • show (bool, optional) – Whether to show the QA plot

pypeit.core.flux_calib.zeropoint_to_throughput(wave, zeropoint, eff_aperture)[source]

Routine to compute the spectrograph throughput from the zeropoint and effective aperture.

Parameters:

  • wave (numpy.ndarray) – Wavelength array shape (nspec,) or (nspec, norders)

  • zeropoint (numpy.ndarray) – Zeropoint array shape (nspec,) or (nspec, norders)

  • eff_aperture (float) – Effective aperture of the telescope in m^2. See spectrograph object

Returns:

throughput – Throughput of the spectroscopic setup. Same shape as wave and zeropoint

Return type:

numpy.ndarray

pypeit.core.flux_calib.zp_unit_const()[source]

This constant defines the units for the spectroscopic zeropoint. See Flux Calibration.