GroupFitter

class grizli.fitting.GroupFitter

Bases: object

Base class for StackFitter and MultiBeam spectrum fitting objects

Methods Summary

check_tfit_coeffs(tfit, templates[, ...])	Compare emission line fluxes fit at each grism/PA to the combined value.
compute_D4000(z[, fit_background, fit_type, ...])	Compute D4000 with step templates
compute_scale_array(pscale, wave)	Return the scale array given the coefficients
fit_line_width([bl, nl, z0, max_nfev, tol, ...])	Fit for emisson line width
get_SDSS_photometry([bands, templ, radius, ...])	Try too get SDSS photometry from astroquery
get_flat_background(bg_params[, apply_mask])	Generate background array the same size as the flattened total science array.
get_flat_model(spectrum_1d[, id, ...])	Generate model array based on the model 1D spectrum in spectrum_1d
initialize_masked_arrays([seg_ids])	Initialize flat masked arrays for faster likelihood calculation
objfun_scale(pscale, AxT, data, self, retval)	Objective function for fitting for a scale term between photometry and spectra
oned_figure([bin, wave, show_beams, minor, ...])	Make a figure showing the 1D spectra
optimal_extract([data, bin, wave, ivar, ...])	Binned optimal extractions by grism with algorithm from Horne 1984
scale_to_photometry([tfit, tol, order, ...])	Compute scale factor between spectra and photometry
set_photometry([flam, eflam, filters, ...])	Set photometry attributes
template_at_z([z, templates, fwhm, ...])	Get the best-fit template at a specified redshift
unset_photometry()	Unset photometry-related attributes
xfit_at_z([z, templates, fitter, ...])	Fit the 2D spectra with a set of templates at a specified redshift.
xfit_redshift([prior, templates, fwhm, ...])	Two-step procedure for fitting redshifts
xfit_star([tstar, spline_correction, ...])	Fit stellar templates
xmake_fit_plot(fit, tfit[, show_beams, bin, ...])	Make a diagnostic plot of the redshift fit

Methods Documentation

check_tfit_coeffs(tfit, templates, refit_others=True, fit_background=True, fitter='nnls', bounded_kwargs={'method': 'bvls', 'tol': 1e-08, 'verbose': 0})

Compare emission line fluxes fit at each grism/PA to the combined value. If refit_others=True, then compare the line fluxes to a fit from a new object generated excluding that grism/PA.

Parameters

tfitdict

Output of template_at_z for the line template.

templatesdict

Dictionary of SpectrumTemplate objects.

refit_othersbool

Refit the other grism/PA combinations.

fit_backgroundbool

Include background in the fit.

fitterstr

Fitting method passed to template_at_z. Default is nnls.

bounded_kwargsdict

Bounded least-squares fitting parameters.

Returns

max_linestr

Line species with the maximum deviation

max_line_difffloat

The maximum deviation for max_line (sigmas).

comparedict

The full comparison dictionary

compute_D4000(z, fit_background=True, fit_type='D4000', fitter='lstsq')

Compute D4000 with step templates

Parameters

zfloat

Redshift where to evaluate D4000

fit_backgroundbool

Include background in step template fit

fit_type‘D4000’, ‘Dn4000’

Definition to use:

D4000 = f_nu(3750-3950) / f_nu(4050-4250) Dn4000 = f_nu(3850-3950) / f_nu(4000-4100)

fitterstr

Least-squares method passed to template_at_z.

Returns

w_d4000, t_d4000ndarray, dict

Step wavelengths and template dictionary

tfitdict

Fit dictionary returned by template_at_z.

d4000, d4000_sigmafloat

D4000 estimate and uncertainty from simple error propagation and step template fit covariance.

static compute_scale_array(pscale, wave)

Return the scale array given the coefficients

Parameters

pscalearray-like

Coefficients of the linear model normalized by factors of 10 per order, i.e, pscale = [10] is a constant unit scaling. Note that parameter is what is expected by numpy.polynomial.Polynomial.

wavearray-like

Wavelength grid in Angstroms. Scaling is normalized to (wave - 1e4)/1000.

Returns

wscalearray-like

Scale factor

>>> pscale = [10]
>>> N = len(pscale)
>>> rescale = 10**(np.arange(N)+1)
>>> wscale = np.polynomial.Polynomial(pscale/rescale)((wave-1.e4)/1000.)

fit_line_width(bl=2.5, nl=1.1, z0=1.9367, max_nfev=100, tol=0.001, verbose=False)

Fit for emisson line width

Parameters

blfloat

Initial guess for broad line width in 1000 km/s.

nlfloat

Initial guess for narrow line width in 1000 km/s.

z0float

Initial guess for redshift.

max_nfevint

Maximum number of function evaluations.

tolfloat

Tolerance for the fit.

verbosebool

Passed to least_squares.

Returns:

reslist

List of results: [bl, nl, z, nfev, max_nfev_reached].

blfloat

Best-fit broad line width in 1000 km/s.

nlfloat

Best-fit narrow line width in 1000 km/s.

zfloat

Best-fit redshift.

nfevint

Number of function evaluations.

max_nfev_reachedbool

True if the maximum number of function evaluations was reached.

get_SDSS_photometry(bands='ugriz', templ=None, radius=2, SDSS_CATALOG='V/147/sdss12', get_panstarrs=False)

Try too get SDSS photometry from astroquery

(developmental)

Parameters

bandsstr

SDSS bands to query.

templdict

Templates to use for fitting.

radiusfloat

Search radius in arcsec.

SDSS_CATALOGstr

Catalog to query. Default is V/147/sdss12.

get_panstarrsbool

Get PanSTARRS photometry.

Returns

photdict

Dictionary with keys flam, eflam, filters, tempfilt, where flam and eflam are arrays of flux densities and uncertainties in f-lambda units, filters are the FilterDefinition objects, and tempfilt is a TemplateGrid object.

get_flat_background(bg_params, apply_mask=True)

Generate background array the same size as the flattened total science array.

Parameters

bg_paramsarray with shape (self.N) or (self.N, M)

Background parameters for each beam, where the M axis is polynomial cofficients in the order expected by Polynomial2D. If the array is 1D, then provide a simple pedestal background.

apply_maskbool

Return the background pixels applying the BeamCutout fit_mask attribute.

Returns

bg_modelArray with dimensions (self.fit_mask.sum(),)

Flattened, masked background array.

get_flat_model(spectrum_1d, id=None, apply_mask=True, is_cgs=True)

Generate model array based on the model 1D spectrum in spectrum_1d

Parameters

spectrum_1dtuple, -1

Tuple of 1D arrays (wavelength, flux). If -1, then use the in_place model attributes of each beam.

idint

Value that identifies pixels in the segmentation thumbnail with the desired object to model

apply_maskbool

Return the model pixels applying the BeamCutout fit_mask attribute

is_cgsbool

spectrum_1d flux array has CGS f-lambda flux density units.

Returns

modelArray with dimensions (self.fit_mask.sum(),)

Flattened, masked model array.

initialize_masked_arrays(seg_ids=None)

Initialize flat masked arrays for faster likelihood calculation

Parameters

seg_idslist

List of segmentation IDs to use for the optimal profile.

static objfun_scale(pscale, AxT, data, self, retval)

Objective function for fitting for a scale term between photometry and spectra

Parameters

pscalearray-like

Scale factor for each template in AxT and the photometry.

AxTndarray

Template array, AxT.T is the transpose of the template array where each column is a template.

datandarray

Photometry data.

selfGroupFitter

Object with the templates and photometry.

retvalstr

Return type, one of [‘resid’, ‘coeffs’].

Returns

if retval == ‘resid’:

weighted residuals

else if retval == ‘coeffs’:

coeffs: array-like

Coefficients of the fit.

full: ndarray

Full model fit.

resid: ndarray

Residuals.

chi2: float

Chi-squared of the fit.

AxT: ndarray

Template array.

else:

chi2: float

Chi-squared of the fit.

oned_figure(bin=1, wave=None, show_beams=True, minor=0.1, tfit=None, show_rest=False, axc=None, figsize=[6, 4], fill=False, units='flam', min_sens_show=0.1, ylim_percentile=2, scale_on_stacked=False, show_individual_templates=False, apply_beam_mask=True, loglam_1d=True, trace_limits=None, show_contam=False, add_label=True, beam_models=None, median_filter_kwargs=None)

Make a figure showing the 1D spectra

Parameters

binfloat

Binning factor relative to nominal resolution (per pix) of each grism

waveNone, array

Fixed wavelength array for the sampled spectra

show_beamsbool

Show all individual beams

minorfloat

Minor axis tick interval (microns)

tfitdict

Fit information from template_at_z. If provided, then will include the best-fit models in the figure

show_restbool

Show rest-frame wavelengths

axcAxesSubplot

If provided, then draw into existing axis without making a new figure

figsize(float, float)

Figure size (inches)

fillbool

plot filled spectra

unitsstr

Y-axis units

• ‘flam’ = Scaled f-lambda cgs

• ‘nJy’ = nanoJansky

• ‘mJy’ = milliJansky

• ‘eps’ = native detector units of electrons per second

• ‘meps’ = “milli”-electrons per second

• ‘spline[N]’ = Divide out a spline continuum

• ‘resid’ = Residuals w.r.t. model in tfit

min_sens_showfloat

Minimum sensitivity to show in the plot.

ylim_percentilefloat

Percentile of the data to use for the y-axis limits.

scale_on_stackedbool

Set the y-axis limits based on the stacked spectrum.

show_individual_templatesbool

Show each individual template with its scaling along with the best-fit combination

apply_beam_maskbool

Apply the beam mask to the 1D spectra.

loglam_1dbool

Plot as log wavelength

trace_limits(float, float)

If provided, extract spectra relative to the (tilted) spectral trace

show_contambool

Include curves for contamination model

add_labelbool

Add a label to the plot.

beam_modelslist

List of 2D arrays with the model for each beam. If not None, then use these models instead of computing them from the beam objects.

median_filter_kwargsdict

Parameters passed to safe_nanmedian_filter to filter the 2D models before extracting the 1D spectrum.

Returns

figFigure

Figure object

optimal_extract(data=None, bin=1, wave=None, ivar=None, trace_limits=None, loglam=True, **kwargs)

Binned optimal extractions by grism with algorithm from Horne 1984

The spatial profile for each beam is the 2D model spectrum generated using its attached direct image thumbnail. The Horne (1984) algorithm is essentially a least-squares fit of the spatial model to the observed 2D spectrum, weighted by the uncertainties.

Along with the optimal extraction, this method also implements an option to extract an effective “aperture” within a specified region above and below the spectral trace.

While the traces may not be directly aligned with the x axis of the 2D spectra, both the optimal and trace extractions extract along y pixels at a given x.

Parameters

datandarray, None

Data array with same dimensions as self.scif_mask (flattened & masked) 2D spectra of all beams. If None, then use self.scif_mask.

binbool

Binning factor relative to the grism-dependent resolution values, specified in GRISM_LIMITS.

wavendarray, None

Wavelength bin edges. If None, then compute from parameters in GRISM_LIMITS.

ivarndarray, None

Inverse variance array with same dimensions as self.scif_mask (flattened & masked) 2D spectra of all beams. If None, then use self.weighted_sigma2_mask.

trace_limits[float, float] or None

If specified, perform a simple sum in cross-dispersion axis between trace_limits relative to the central pixel of the trace rather than the optimally-weighted sum. Similarly, the output variances are the sum of the input variances in the trace interval.

Note that the trace interval is evaluated with < >, as opposed to <= >=, as the center of the trace is a float rather than an integer pixel index.

loglambool

If True and wave not specified (see above), then output wavelength grid is log-spaced.

Returns

tabdict

Dictionary of Table spectra for each available grism.

scale_to_photometry(tfit=None, tol=0.0001, order=0, init=None, fit_background=True, Rspline=50, use_fit=True, **kwargs)

Compute scale factor between spectra and photometry

Parameters

tfitdict

Template fit info at a specific redshift from template_at_z. If not specified, then makes and fits spline templates

tolfloat

Fit tolerance passed to the minimizer

orderint

Order of the polynomial scaling to fit

initNone

Initial parameters

fit_backgroundbool

Include additive background

Rsplinefloat

Spectral resolution R of spline templates

use_splinebool

Use spline templates

Returns

resobject

Result from scipy.optimize.least_squares. The coefficients of the linear scaling are in res.x.

set_photometry(flam=[], eflam=[], filters=[], ext_corr=1, force=False, tempfilt=None, min_err=0.02, TEF=None, pz=None, source='unknown', **kwargs)

Set photometry attributes

Parameters

flam, eflamarray-like

Flux densities and uncertainties in f-lambda cgs units

filterslist

List of FilterDefinition objects

ext_corrfloat or array-like

MW extinction correction

lcarray-like

Precomputed filter central wavelengths. Will automatically be computed from filters if not specified

forcebool

Don’t try to set if already specified (Nphot > 0)

tempfilteazy.photoz.TemplateGrid

Precomputed grid of templates integrated through the filters bandpasses

min_errfloat

minimum or systematic error to add in quadrature to eflam

TEFeazy.templates.TemplateError

Template error function

pzNone, (array, array)

Precomputed (z, pz) pdf from, e.g., eazy (not used)

sourcestr

String to indicate the provenance of the photometry

Returns

photom_flamarray_like

Flux densities from flam

photom_eflamarray-like

Uncertainties including min_err

photom_filterslist

filters

Nphotint

Number of photometry bandpasses

The returned parameters above are not returned but are rather

attributes that are set. This function also updates the

sivarf, weightf, fit_mask attributes to include the

spectra + photometry

template_at_z(z=0, templates=None, fwhm=1400, get_uncertainties=2, draws=0, **kwargs)

Get the best-fit template at a specified redshift

Parameters

zfloat

Redshift

templatesdict

Dictionary of SpectrumTemplate objects

fwhmfloat

FWHM of line templates if templates generated in-place

get_uncertaintiesint

Get coefficient uncertainties from covariance matrix. See xfit_at_z.

drawsint

Number of random draws from covariance matrix

kwargsdict

Any additional keywords are passed to xfit_at_z

Returns

tfitdict

Dictionary of fit results, used in various other places like oned_figure, etc.

Keyword	Description
cfit	Dict of template normalizations and uncertainties
cont1d	SpectrumTemplate of best-fit continuum
line1d	SpectrumTemplate of best-fit continuum + emission line
coeffs	Array of fit coefficients
chi2	(float) chi-squared of the fit
z	(float) The input redshift
templates	Copy of the input templates dictionary
line1d_err	If draws > 0, this will be template draws with the same dimension as line1d

unset_photometry()

Unset photometry-related attributes

xfit_at_z(z=0, templates=[], fitter='nnls', fit_background=True, get_uncertainties=False, get_design_matrix=False, pscale=None, COEFF_SCALE=1e-19, get_components=False, huber_delta=4, get_residuals=False, include_photometry=True, use_cached_templates=False, bounded_kwargs={'method': 'bvls', 'tol': 1e-08, 'verbose': 0}, apply_sensitivity=True, median_filter_kwargs=None)

Fit the 2D spectra with a set of templates at a specified redshift.

Parameters

zfloat

Redshift.

templateslist

List of templates to fit.

fitterstr

Minimization algorithm to compute template coefficients.

Available options are:

• ‘nnls’, Non-negative least squares (scipy.optimize.nnls)

• ‘lstsq’, Standard least squares (numpy.linalg.lstsq)

• ‘bounded’, Bounded least squares (scipy.optimize.lsq_linear)

For the last option, the line flux limits are set by the limits in the global grizli.fitting.LINE_BOUNDS list and bounded_kwargs are passed to lsq_linear.

fit_backgroundbool

Fit additive pedestal background offset.

get_uncertaintiesbool, int

Compute coefficient uncertainties from the covariance matrix. If specified as an int > 1, then the covariance matrix is computed only for templates with non-zero coefficients

get_design_matrixbool

Return design matrix and data, rather than nominal outputs.

pscalefloat, array-like

Scale parameters passed to compute_scale_array. If None, then try to use from self.pscale.

COEFF_SCALEfloat

Scale factor for the template coefficients. The coefficients are stored as coeffs * COEFF_SCALE to improve numerical precision.

get_componentsbool

Return the individual components of the fit.

huber_deltafloat

Use the Huber loss function (scipy.special.huber) rather than direct chi-squared. If huber_delta < 0, then fall back to chi-squared.

get_residualsbool

Return the residuals of the fit.

include_photometrybool

Include photometry in the fit.

use_cached_templatesbool

Use cached template arrays for speedup.

bounded_kwargsdict

Keyword arguments passed to lsq_linear.

apply_sensitivitybool

Apply the sensitivity function to the templates.

median_filter_kwargsdict

Keyword arguments passed to safe_nanmedian_filter.

Returns

AxTnumpy.ndarray

Transposed design matrix, weighted by 1/sigma. (If get_design_matrix  == True).

datanumpy.ndarray

Masked data array, including background pedestal. (If get_design_matrix  == True)

modelnumpy.ndarray

Full model array, including the contributions from all templates. (If get_components  == True)

backgroundnumpy.ndarray

Background array computed from the fit. (If get_components  == True)

chi2float

Chi-squared of the fit, computed using the Huber loss function if huber_delta > 0.

coeffsnumpy.ndarray

Template coefficients from the fit.

coeffs_errnumpy.ndarray

Uncertainties of the template coefficients.

covariancenumpy.ndarray

Full covariance matrix of the fit parameters.

xfit_redshift(prior=None, templates={}, fwhm=1200, line_complexes=True, fsps_templates=False, zr=[0.65, 1.6], dz=[0.005, 0.0004], zoom=True, verbose=True, fit_background=True, fitter='nnls', bounded_kwargs={'method': 'bvls', 'tol': 1e-08, 'verbose': 0}, delta_chi2_threshold=0.004, poly_order=3, use_cached_templates=True, get_uncertainties=True, Rspline=30, huber_delta=4, get_student_logpdf=False)

Two-step procedure for fitting redshifts

1. polynomial, spline template fits

2. redshift grids

3. …

Parameters

priorNone, (array, array)

Redshift prior (z, pz). Will be interpolated to the redshift fit grid

templatesdict

Dictionary the SpectrumTemplate objects to use for the fits

fwhm, line_complexes, fsps_templatesfloat, bool, bool

Parameters passed to load_templates if templates is empty.

zr(float, float)

Redshift limits of the logarithmic (1+z) redshift grid

dz(float, float)

Step size of the grid. The second value will be used to “zoom in” on the peaks found in the coarse grid step from the first value.

zoombool

Do the second pass with the dz[1] step size

verbosebool

Some verbosity control

fit_backgroundbool

Include contribution of additive background

fitter, bounded_kwargsstr, dict

Least-squares optimization method. See xfit_at_z

delta_chi2_thresholdfloat

Not used

poly_orderint

Order of polynomials for the “uninformative” polynomial fit. The parameters of the polynomial and full template fits are computed to evaluate the extent to which the galaxy / stellar templates improve the fit

use_cached_templatesbool

Try to used cached versions of dispersed template models for templates that don’t depend on redshift (polynomials, splines)

get_uncertaintiesbool

Get template fit coefficient uncertainties from the fit covariance matrix

Rsplinefloat

Spectral resolution, R, of spline templates for another “uninformative” fit.

huber_deltafloat

Parameter for Huber loss function (see xfit_at_z)

get_student_logpdfbool

Get logpdf for likelihood assuming Student-t distribution rather than standard normal assumption

Returns

fitTable

Table with fit information on the redshift grid and metadata on some fit characteristics.

Table metadata

Meta	Description
N	Number of spectrum extensions / beams
polyord	Order of the polynomial fit
chi2poly	\(\chi^2\) of the polynomial fit
chi2spl	\(\chi^2\) of the spline fit
Rspline	Spectral resolution of the spline templates
kspl	Effective number of parameters of spline fit
huberdel	huber_delta
splf[i]	Flux of spline fit at fixed wavelengths
sple[i]	Unc. of spline fit at fixed wavelengths
NTEMP	Number of templates
DoF	Degrees of freedom of the fit (total number of unmasked pixels in all 2D beams)
ktempl	N parameters of the template fit
chimin	Minimum \(\chi^2\) of the template fit
chimax	Maximum \(\chi^2\) of the template fit
fitter	Least squares method
as_epsf	Fit was done as EffectivePSF
bic_poly	Bayesian Information Criterion (BIC) of the polynomial fit. BIC = log(DoF)*k + min(chi2) + C
bic_spl	BIC of the spline fit
bic_temp	BIC of the template (redshift) fit
st_df	Student-t df of spline fit
st_loc	Student-t loc of spline fit
st_scl	Student-t scale of spline fit
Z02	Integrated cdf(<z) = 0.025
Z16	Integrated cdf(<z) = 0.16
Z50	Integrated cdf(<z) = 0.50
Z84	Integrated cdf(<z) = 0.84
Z97	Integrated cdf(<z) = 0.975
ZWIDTH1	Z84 - Z16
ZWIDTH2	Z97 - Z02
z_map	Redshift at Max(PDF)
zrmin	Start of the redshift grid zr
zrmax	End of the redshift grid zr
z_risk	Redshift at minimum risk
min_risk	Minimum risk
gam_loss	gamma parameter of risk

Column	Description
zgrid	Redshift grid NZ
chi2	\(\chi^2(z)\) NZ
student_logpdf	log PDF of student-t likelihood NZ
coeffs	Template coefficients (NZ,NTEMP)
covar	Template covariance (NZ,NTEMP,NTEMP)
pdf	Full likelihood including optional prior
risk	“Risk” parameter from Tanaka et al. (arXiv/1704.05988)

xfit_star(tstar=None, spline_correction=True, fitter='nnls', fit_background=True, spline_args={'Rspline': 5}, oned_args={})

Fit stellar templates

Parameters

tstardict

Dictionary of stellar SpectrumTemplate objects

spline_correctionbool

Include spline scaling correction for template mismatch

fitterstr

Least-squares method passed to template_at_z.

fit_backgroundbool

Fit for additive background component

spline_argsdict

Parameters passed to split_spline_template for generating the spline correction arrays

oned_argsdict

Keywords passed to oned_figure

Returns

figFigure

Figure object

linestr

Line of text describing the best fit

tfitdict

Fit information from template_at_z

xmake_fit_plot(fit, tfit, show_beams=True, bin=1, minor=0.1, scale_on_stacked_1d=True, loglam_1d=True, zspec=None)

Make a diagnostic plot of the redshift fit

Parameters

fitTable

Redshift fit results from xfit_redshift

tfitdict

Template fit at best redshift from template_at_z

show_beamsbool

Show 1D spectra of all individual “beams”

binfloat

Binning factor relative to nominal wavelength resolution (1 pix) of each grism

minorfloat

Minor axis ticks, microns

scale_on_stacked_1dbool

Set y limits based on stacked spectrum

loglam_1dbool

Show log wavelengths

zspecfloat, None

Spectroscopic redshift that will be indicated on the figure

Returns

figFigure

Figure object