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.

polyxlist

Polynomial coefficients to use for the compute_model method.

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

mask_residbool

Mask residuals above a certain threshold.

verbosebool

Print verbose information.

replace_directdict

Replace the direct image cutouts with a new image. Keys are ref_image, interp, cutout, and background_func.

restore_medfiltbool

Restore median-filtered direct image cutouts.

Attributes

Asavedict

Dictionary to store the results of the fit.

Attributes Summary

N	Length of the beam list.
Ngrism	Dictionary containing number of exposures by grism.
PA	Available PAs in each grism
grisms	Available grisms
id	ID of the first beam in the list.

Methods Summary

apply_trace_shift([set_to_zero])	Apply the current beam.yoffset to the trace of each beam.
check_for_bad_PAs([poly_order, ...])	Check for bad PAs based on chi-squared values
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, ...])	Drizzle line maps from the individual beams.
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, ...)	Evaluate the trace shifts.
extend(new[, verbose])	Concatenate MultiBeam objects
fit_at_z([z, templates, fitter, ...])	Fit the model at a given redshift.
fit_redshift([prior, poly_order, fwhm, ...])	Fit redshifts for the object.
fit_stars([poly_order, fitter, ...])	Fit stellar templates to the data.
fit_trace_shift([split_groups, max_shift, ...])	Fit a global trace shift to the beams.
flag_with_drizzled(hdul[, sigma, update, ...])	Update MultiBeam masks based on the blotted drizzled combined image
init_poly_coeffs([flat, poly_order])	Initialize polynomial coefficients for the continuum fit.
load_beam_fits(beam_list[, conf, verbose])	Load a list of individual beam cutouts.
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])	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)	Reshape a flattened array into a list of 2D arrays.
run_full_diagnostics([pzfit, pspec2, pline, ...])	Run full redshift fit and diagnostics on a GroupFLT object.
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, ...])	Show redshift fit.
write_beam_fits([verbose])	Write individual beam cutouts to FITS files.
write_master_fits([verbose, get_hdu, strip, ...])	Store all beams in a single HDU

Attributes Documentation

N

Length of the beam list.

Ngrism

Dictionary containing number of exposures by grism.

PA

Available PAs in each grism

grisms

Available grisms

id

ID of the first beam in the list.

Methods Documentation

apply_trace_shift(set_to_zero=False)

Apply the current beam.yoffset to the trace of each beam.

Parameters

set_to_zerobool

Set beam.yoffset to zero

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

Check for bad PAs based on chi-squared values

Parameters

poly_orderint

Polynomial order for the background fit.

chi2_thresholdfloat

Threshold for chi-squared ratio to the minimum value.

fit_backgroundbool

Fit a polynomial background.

reinitbool

Reinitialize the MultiBeam object with thes beams that pass the threshold.

Returns

fit_logdict

Log of the fit results.

keep_dictdict

Dictionary of PAs to keep.

has_badbool

True if any PAs are flagged as bad.

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.

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

Parameters

idstr

Object ID.

spectrum_1darray-like

1D spectrum to use for the model.

is_cgsbool

Spectrum is in cgs units.

apply_sensitivitybool

Apply the sensitivity function to the model.

scalefloat

Scale factor for the model.

resetbool

Reset the model to zero before computing the new model.

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.

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

Parameters

idstr

Object ID.

spectrum_1darray-like

1D spectrum to use for the model.

is_cgsbool

Spectrum is in cgs units.

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)

Drizzle line maps from the individual beams.

Parameters

fitdict

Fit data generated by fit_redshift.

plinelist

List of line names to drizzle.

force_linelist

List of line names to always drizzle.

save_fitsbool

Save the drizzled line maps to FITS files.

mask_linesbool

Mask pixels around other lines.

mask_sn_limitfloat

Mask pixels around lines with S/N below this limit.

mask_4959bool

Mask OIII-4959 if detected.

verbosebool

Print status messages.

include_segmentationbool

Drizzle the segmentation image.

get_ir_psfsbool

Get the IR PSFs from the MultiBeam.beams.

min_line_snfloat

Minimum S/N to drizzle a line.

Returns

hdu_fulllist

List of HDUList objects with the drizzled line maps.

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

Parameters

sizeint

Image size in pixels

fcontamfloat

Contamination parameter for drizzle.

flambdabool

Convert to f-lambda units.

scalefloat

Scale factor for drizzle.

pixfracfloat

Drizzle pixfrac parameter.

kernelstr

Drizzle kernel parameter.

usewcsbool

Use WCS information for drizzle.

tfitdict

Template fit results.

diffbool

Show difference from the model.

grism_listlist

List of grisms to include.

mask_segmentationbool

Mask segmentation regions.

reset_modelbool

Reset the model to a flat spectrum.

make_figurebool

Make the figure.

fig_argsdict

Arguments for the figure.

Returns

figFigure

Figure object.

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

Parameters

idint

ID to drizzle.

wcsobjWCS or Header

Output WCS.

kernele.g., ‘square’, ‘point’, ‘gaussian’

Drizzle kernel, see drizzle.

pixfracfloat

Drizzle ‘pixfrac’, see drizzle.

verbosebool

Print status messages.

Returns

drizzled_segmndarray, type int64.

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

eval_poly_spec(coeffs_full)

Evaluate polynomial spectrum.

Parameters

coeffs_fullarray-like

Full list of polynomial coefficients.

Returns

xspec, yfullarray-like

Arrays of wavelength and polynomial spectrum.

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

Evaluate the trace shifts.

Parameters

shiftsndarray

Array of trace shifts.

selfMultiBeam

Object to evaluate.

indiceslist

List of indices to evaluate.

poly_orderint

Polynomial order to fit.

lmbool

Use Levenberg-Marquardt optimization.

verbosebool

Print status messages.

fit_with_psfbool

Fit with the PSF model.

Returns

chi2float

Chi-squared value.

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)

Fit the model at a given redshift.

Parameters

zfloat

Redshift to fit.

templatesdict

Dictionary of template spectra to fit.

fitterstr

Fitting method. Options are ‘nnls’ and ‘lstsq’.

fit_backgroundbool

Fit a constant background.

poly_orderint

Order of the polynomial to fit.

Returns

out_coeffsarray-like

Output coefficients of the fit.

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)

Fit redshifts for the object.

Parameters

prior: list or array-like or None

A list or array-like object containing two elements:

• prior[0]: The x-coordinates for interpolation.

• prior[1]: The y-coordinates for interpolation.

Used to compute interpolated values over the zgrid using numpy.interp when not None.

poly_orderint

Polynomial order to fit.

fwhmfloat

FWHM of the line spread function.

make_figurebool

Generate a figure of the fit.

zrlist or None

Redshift range to fit.

dzlist or None

Redshift step size.

verbosebool

Print information to the terminal.

fit_backgroundbool

Fit a constant background.

fitterstr

Fitting method. Options are ‘nnls’ and ‘lstsq’.

delta_chi2_thresholdfloat

Threshold for computing the redshift grid. Not used.

zoombool

Zoom in on the best-fit redshift.

line_complexesbool

Include line complexes in the fit.

templatesdict

Dictionary of template spectra to fit.

figsizelist

Figure size for the output plot.

fsps_templatesbool

Use FSPS templates.

Returns

fit_datadict

Fit data.

figFigure

Figure object.

fit_stars(poly_order=1, fitter='nnls', fit_background=True, verbose=True, make_figure=True, fwhm=0, **kwargs)

Fit stellar templates to the data.

Parameters

poly_orderint

Polynomial order to fit.

fitterstr

Fitting method. Options are ‘nnls’ and ‘lstsq’.

fit_backgroundbool

Fit a constant background.

verbosebool

Print information to the terminal.

make_figurebool

Generate a figure of the fit.

zoombool

Zoom in on the best-fit redshift. Not used.

delta_chi2_thresholdfloat

Threshold for computing the redshift grid. Not used.

zrfloat

Redshift to fit. Not used.

dzfloat

Redshift step size. Not used.

fwhmfloat

FWHM of the line spread function.

priorNone

Not used.

templatesdict

Dictionary of template spectra to fit.

figsizelist

Figure size for the output plot. Not used.

fsps_templatesbool

Not used.

Returns

fit_datadict

Fit data.

figFigure

Figure object.

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

Fit a global trace shift to the beams.

Parameters

split_groupsbool

Split the fit into groups.

max_shiftfloat

Maximum allowed shift in pixels.

tolfloat

Tolerance for the fit.

verbosebool

Print status messages.

lmbool

Use Levenberg-Marquardt optimization.

fit_with_psfbool

Fit with the PSF model.

resetbool

Reset the trace shifts to zero.

Returns

shiftsndarray

Array of trace shifts.

outndarray

Output from the optimization routine.

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.

verbosebool

Print status messages.

Returns

Updates the individual fit_mask attributes of the individual beams

if update==True.

init_poly_coeffs(flat=None, poly_order=1)

Initialize polynomial coefficients for the continuum fit.

Parameters

flatarray-like

Flattened array of the flat f-lambda model.

poly_orderint

Order of the polynomial to fit.

Returns

None (updates self.A_poly, self.n_poly, self.x_poly).

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

Load a list of individual beam cutouts.

Parameters

beam_listlist

List of filenames to load.

confGroupFitter

Configuration object to use for the beam cutouts.

verbosebool

Print the name of the file being loaded.

load_master_fits(beam_file, verbose=True)

Load a “beams.fits” file.

Parameters

beam_filestr

Filename of the “beams.fits” file.

verbosebool

Print the name of the file being loaded.

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

tfitdict

Output of template_at_z.

get_contambool

Extract the contamination model.

get_backgroundbool

Extract the background model.

masked_modelndarray

Masked model to extract.

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, **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

Parameters

fitdict

Fit data generated by fit_redshift.

spatial_scalefloat

Scale factor for the spatial axis.

dlamfloat

Pixel size in Angstroms.

NYint

Number of pixels in the spatial direction.

figsizelist

Figure size for the output plot.

Returns

figFigure

Figure object.

hdu_sciHDUList

Drizzled 2D spectrum.

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.

extint

Extension of the ref_image to use.

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]).

thumb_labelslist

List of thumbnail labels to replace.

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)

Reshape a flattened array into a list of 2D arrays.

Parameters

flat_arrayarray-like

Flattened array to reshape.

Returns

outlist

List of 2D arrays.

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

Run full redshift fit and diagnostics on a GroupFLT object.

Parameters

pzfitdict

Parameters for the redshift fit.

pspec2dict

Parameters for the 2D spectrum drizzle.

plinedict

Parameters for the line drizzle.

force_linelist

List of line names to always drizzle.

GroupFLTGroupFLT

Group object with the full set of objects to fit simultaneously.

priorTable

Prior catalog for the redshift fit.

zoombool

Zoom in on the object.

verbosebool

Print status messages.

Returns

figFigure

Figure object.

hdu2HDUList

Drizzled 2D spectrum.

hdu_fullHDUList

Drizzled line maps.

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])

Show redshift fit.

Parameters

fit_datadict

Fit data generated by fit_redshift.

plot_flambdabool

Plot in f-lambda units.

figsizelist

Figure size for the output plot.

Returns

figFigure

Figure object.

write_beam_fits(verbose=True)

Write individual beam cutouts to FITS files.

Parameters

verbosebool

Print the name of the output file.

Returns

outfileslist

List of output filenames.

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

Store all beams in a single HDU

Parameters

verbosebool

Print the name of the output file.

get_hdubool

Return the HDUList object rather than writing to a file.

stripbool

Strip the HDUList object before writing.

include_modelbool

Include the model arrays in the output file.

get_trace_tablebool

Include the trace table in the output file.

Returns

hduHDUList

Output HDUList object if get_hdu=True.