MultiBeam

class grizli.multifit.MultiBeam(beams, group_name=None, fcontam=0.0, psf=False, polyx=[0.3, 2.5], MW_EBV=0.0, min_mask=0.01, min_sens=0.08, sys_err=0.0, mask_resid=True, verbose=True, replace_direct=None, restore_medfilt=False, **kwargs)

Bases: GroupFitter

Tools for dealing with multiple BeamCutout instances

Parameters

beamslist

List of BeamCutout objects.

group_namestr, None

Rootname to use for saved products. If None, then default to ‘group’.

fcontamfloat

Factor to use to downweight contaminated pixels. The pixel inverse variances are scaled by the following weight factor when evaluating chi-squared of a 2D fit,

weight = np.exp(-(fcontam*np.abs(contam)*np.sqrt(ivar)))

where contam is the contaminating flux and ivar is the initial pixel inverse variance.

psfbool

Fit an ePSF model to the direct image to use as the morphological reference.

MW_EBVfloat

Milky way foreground extinction.

min_maskfloat

Minimum factor relative to the maximum pixel value of the flat f-lambda model where the 2D cutout data are considered good. Passed through to BeamCutout.

min_sensfloat

See BeamCutout.

sys_errfloat

Systematic error added in quadrature to the pixel variances:

var_total = var_initial + (beam.sci*sys_err)**2

Attributes

TBDtype

Attributes Summary

N
Ngrism	dictionary containing number of exposures by grism
PA	Available PAs in each grism
grisms	Available grisms
id

Methods Summary

apply_trace_shift([set_to_zero])	Set beam.yoffset back to zero
check_for_bad_PAs([poly_order, ...])
compute_exptime()	Compute number of exposures and total exposure time for each grism
compute_model([id, spectrum_1d, is_cgs, ...])	Compute the dispersed 2D model for an assumed input spectrum
compute_model_psf([id, spectrum_1d, is_cgs])	Compute the dispersed 2D model for an assumed input spectrum and for ePSF morphologies
drizzle_fit_lines(fit, pline[, force_line, ...])	TBD
drizzle_grisms_and_PAs([size, fcontam, ...])	Make figure showing spectra at different orients/grisms
drizzle_segmentation([wcsobj, kernel, ...])	Drizzle segmentation image from individual MultiBeam.beams.
drizzle_segmentation_id([id, wcsobj, ...])	Drizzle segmentation image for a single ID
eval_poly_spec(coeffs_full)	Evaluate polynomial spectrum
eval_trace_shift(shifts, self, indices, ...)	TBD
extend(new[, verbose])	Concatenate MultiBeam objects
fit_at_z([z, templates, fitter, ...])	TBD
fit_redshift([prior, poly_order, fwhm, ...])	TBD
fit_stars([poly_order, fitter, ...])	TBD
fit_trace_shift([split_groups, max_shift, ...])	TBD
flag_with_drizzled(hdul[, sigma, update, ...])	Update MultiBeam masks based on the blotted drizzled combined image
init_poly_coeffs([flat, poly_order])	TBD
load_beam_fits(beam_list[, conf, verbose])	TBD
load_master_fits(beam_file[, verbose])	Load a "beams.fits" file.
make_simple_hdulist()	Make a`~astropy.io.fits.HDUList` object with just a simple PrimaryHDU
oned_spectrum([tfit, get_contam, ...])	Compute full 1D spectrum with optional best-fit model
oned_spectrum_to_hdu([sp, outputfile, units])	Generate 1D spectra fits HDUList
parse_fit_outputs(z, templates, coeffs_full, A)	Parse output from fit_at_z.
redshift_fit_twod_figure(fit[, ...])	Make figure of 2D spectrum
replace_direct_image_cutouts([ref_image, ...])	Replace "REF" extensions in a beams.fits file
replace_segmentation_image_cutouts([ref_image])	Replace "REF" extensions in a beams.fits file
reshape_flat(flat_array)	TBD
run_full_diagnostics([pzfit, pspec2, pline, ...])	TBD
run_individual_fits([z, templates])	Run template fits on each exposure individually to evaluate variance in line and continuum fits.
show_redshift_fit(fit_data[, plot_flambda, ...])	TBD
write_beam_fits([verbose])	TBD
write_master_fits([verbose, get_hdu, strip, ...])	Store all beams in a single HDU TBD

Attributes Documentation

N

Ngrism

dictionary containing number of exposures by grism

PA

Available PAs in each grism

grisms

Available grisms

id

Methods Documentation

apply_trace_shift(set_to_zero=False)

Set beam.yoffset back to zero

check_for_bad_PAs(poly_order=1, chi2_threshold=1.5, fit_background=True, reinit=True)

compute_exptime()

Compute number of exposures and total exposure time for each grism

compute_model(id=None, spectrum_1d=None, is_cgs=False, apply_sensitivity=True, scale=None, reset=True)

Compute the dispersed 2D model for an assumed input spectrum

This is a wrapper around the grizli.model.GrismDisperser.compute_model method, where the parameters are described.

Nothing returned, but the model and modelf attributes are updated on the GrismDisperser subcomponents of the beams list.

compute_model_psf(id=None, spectrum_1d=None, is_cgs=False)

Compute the dispersed 2D model for an assumed input spectrum and for ePSF morphologies

This is a wrapper around the grizli.model.GrismDisperser.compute_model_psf method, where the parameters are described.

Nothing returned, but the model and modelf attributes are updated on the GrismDisperser subcomponents of the beams list.

drizzle_fit_lines(fit, pline, force_line=['Ha+NII', 'Ha', 'OIII', 'Hb', 'OII'], save_fits=True, mask_lines=True, mask_sn_limit=3, mask_4959=True, verbose=True, include_segmentation=True, get_ir_psfs=True, min_line_sn=4)

TBD

drizzle_grisms_and_PAs(size=10, fcontam=0, flambda=False, scale=1, pixfrac=0.5, kernel='square', usewcs=False, tfit=None, diff=True, grism_list=['G800L', 'G102', 'G141', 'GR150C', 'GR150R', 'F090W', 'F115W', 'F150W', 'F200W', 'F356W', 'F300M', 'F335M', 'F360M', 'F410M', 'F430M', 'F460M', 'F480M', 'F444W'], mask_segmentation=True, reset_model=True, make_figure=True, fig_args={'average_only': False, 'cmap': 'viridis_r', 'mask_segmentation': True, 'scale_size': 1}, **kwargs)

Make figure showing spectra at different orients/grisms

TBD

drizzle_segmentation(wcsobj=None, kernel='square', pixfrac=1, verbose=False)

Drizzle segmentation image from individual MultiBeam.beams.

Parameters

wcsobj: `~astropy.wcs.WCS` or `~astropy.io.fits.Header`

Output WCS.

kernel: e.g., ‘square’, ‘point’, ‘gaussian’

Drizzle kernel, see drizzle.

pixfrac: float

Drizzle ‘pixfrac’, see drizzle.

verbose: bool

Print status messages.

Returns

drizzled_segm: ndarray, type int64.

Drizzled segmentation image, with image dimensions and WCS defined in wcsobj.

drizzle_segmentation_id(id=None, wcsobj=None, kernel='square', pixfrac=1, verbose=True)

Drizzle segmentation image for a single ID

eval_poly_spec(coeffs_full)

Evaluate polynomial spectrum

static eval_trace_shift(shifts, self, indices, poly_order, lm, verbose, fit_with_psf)

TBD

extend(new, verbose=True)

Concatenate MultiBeam objects

Parameters

newMultiBeam

Beam object containing new beams to add.

verbosebool

Print summary of the change.

fit_at_z(z=0.0, templates={}, fitter='nnls', fit_background=True, poly_order=0)

TBD

fit_redshift(prior=None, poly_order=1, fwhm=1200, make_figure=True, zr=None, dz=None, verbose=True, fit_background=True, fitter='nnls', delta_chi2_threshold=0.004, zoom=True, line_complexes=True, templates={}, figsize=[8, 5], fsps_templates=False)

TBD

fit_stars(poly_order=1, fitter='nnls', fit_background=True, verbose=True, make_figure=True, zoom=None, delta_chi2_threshold=0.004, zr=0, dz=0, fwhm=0, prior=None, templates={}, figsize=[8, 5], fsps_templates=False)

TBD

fit_trace_shift(split_groups=True, max_shift=5, tol=0.01, verbose=True, lm=False, fit_with_psf=False, reset=False)

TBD

flag_with_drizzled(hdul, sigma=4, update=True, interp='nearest', verbose=True)

Update MultiBeam masks based on the blotted drizzled combined image

[in progress … xxx]

Parameters

hdulHDUList

FITS HDU list output from drizzle_grisms_and_PAs or read from a stack.fits file.

sigmafloat

Residual threshold to flag.

updatebool

Update the mask.

interpstr

Interpolation method for ablot.

Returns

Updates the individual fit_mask attributes of the individual beams

if update==True.

init_poly_coeffs(flat=None, poly_order=1)

TBD

load_beam_fits(beam_list, conf=None, verbose=True)

TBD

load_master_fits(beam_file, verbose=True)

Load a “beams.fits” file.

make_simple_hdulist()

Make a`~astropy.io.fits.HDUList` object with just a simple PrimaryHDU

oned_spectrum(tfit=None, get_contam=True, get_background=False, masked_model=None, **kwargs)

Compute full 1D spectrum with optional best-fit model

Parameters

binfloat / int

Bin factor relative to the size of the native spectral bins of a given grism.

tfitdict

Output of template_at_z.

Returns

spdict

Dictionary of the extracted 1D spectra. Keys are the grism names and the values are Table objects.

oned_spectrum_to_hdu(sp=None, outputfile=None, units=None, **kwargs)

Generate 1D spectra fits HDUList

Parameters

spoptional, dict

Output of oned_spectrum. If None, then run that function with **kwargs.

outputfileNone, str

If a string supplied, then write the HDUList to a file.

Returns

hdulHDUList

FITS version of the 1D spectrum tables.

parse_fit_outputs(z, templates, coeffs_full, A)

Parse output from fit_at_z.

Parameters

zfloat

Redshift at which to evaluate the fits.

templateslist of SpectrumTemplate objects

Generated with, e.g., load_templates.

coeffs_fullndarray

Template fit coefficients

Andarray

Matrix generated for fits and used for computing model 2D spectra:

>>> model_flat = np.dot(coeffs_full, A)
>>> # mb = MultiBeam(...)
>>> all_models = mb.reshape_flat(model_flat)
>>> m0 = all_models[0] # model for mb.beams[0]

Returns

line_fluxdict

Line fluxes and uncertainties, in cgs units (erg/s/cm2)

covarndarray

Covariance matrix for the fit coefficients

cont1d, line1d, model1dSpectrumTemplate

Best-fit continuum, line, and full (continuum + line) templates

model_continuumndarray

Flat array of the best fit 2D continuum

redshift_fit_twod_figure(fit, spatial_scale=1, dlam=46.0, NY=10, figsize=[8, 3.5], **kwargs)

Make figure of 2D spectrum

TBD

replace_direct_image_cutouts(ref_image='gdn-100mas-f160w_drz_sci.fits', ext=0, interp='poly5', cutout=200, background_func=<function median>, thumb_labels=None)

Replace “REF” extensions in a beams.fits file

Parameters

ref_imagestr or HDUList

Filename or preloaded FITS file.

interpstr

Interpolation function to use for do_blot.

cutoutint

Make a slice of the ref_image with size [-cutout,+cutout] around the center position of the desired object before passing to blot.

background_funcfunction, None

If not None, compute local background with value from background_func(ref_image[cutout]).

Returns

beams_imageHDUList

Image object with the “REF” extensions filled with the new blotted image cutouts.

replace_segmentation_image_cutouts(ref_image='gdn-100mas-f160w_seg.fits')

Replace “REF” extensions in a beams.fits file

Parameters

ref_imagestr, HDUList, ImageHDU

Filename or preloaded FITS file.

Returns

beams_imageHDUList

Image object with the “REF” extensions filled with the new blotted image cutouts.

reshape_flat(flat_array)

TBD

run_full_diagnostics(pzfit={}, pspec2={}, pline={}, force_line=['Ha+NII', 'Ha', 'OIII', 'Hb', 'OII'], GroupFLT=None, prior=None, zoom=True, verbose=True)

TBD

size=20, pixscale=0.1, pixfrac=0.2, kernel=’square’

run_individual_fits(z=0, templates={})

Run template fits on each exposure individually to evaluate variance in line and continuum fits.

Parameters

zfloat

Redshift at which to evaluate the fit

templateslist of SpectrumTemplate objects

Generated with, e.g., load_templates.

Returns

line_flux, line_errdict

Dictionaries with the measured line fluxes and uncertainties for each exposure fit.

coeffs_listndarray [Nbeam x Ntemplate]

Raw fit coefficients

chi2_list, DoF_listndarray [Nbeam]

Chi-squared and effective degrees of freedom for each separate fit

show_redshift_fit(fit_data, plot_flambda=True, figsize=[8, 5])

TBD

write_beam_fits(verbose=True)

TBD

write_master_fits(verbose=True, get_hdu=False, strip=True, include_model=False, get_trace_table=True)

Store all beams in a single HDU TBD