FastSpecFit API

fastspecfit

Tools for fast stellar continuum, emission-line, and broadband photometric modeling.

fastspecfit.continuum

Methods and tools for continuum-fitting.

class fastspecfit.continuum.ContinuumFit(ssptemplates=None, metallicity='Z0.0190', minwave=None, maxwave=300000.0, nolegend=False, cache_vdisp=True, solve_vdisp=False, cache_SSPgrid=True, constrain_age=True, mapdir=None)

_fnnls_parallel(modelflux, flux, ivar, xparam=None, debug=False, interpolate_coeff=False, xlabel=None)

Wrapper on fnnls to set up the multiprocessing.

Works with both spectroscopic and photometric input and with both 2D and 3D model spectra.

To be documented.

interpolate_coeff - return the interpolated coefficients when exploring

an array or grid of xparam

continuum_fastphot(data, minbands=3)

Fit the broad photometry.

Parameters

• data (dict) – Dictionary of input spectroscopy (plus ancillary data) populated by unpack_one_spectrum.

• minbands (int, defaults to 3) – Minimum number of photometric bands required to attempt a fit.

Returns

• astropy.table.Table – Table with all the continuum-fitting results with columns documented in init_phot_output.

• .. note:: – See https://github.com/mikeiovine/fast-nnls for the fNNLS algorithm(s).

continuum_specfit(data)

Fit the non-negative stellar continuum of a single spectrum.

Parameters

data (dict) – Dictionary of input spectroscopy (plus ancillary data) populated by fastspecfit.io.DESISpectra.read_and_unpack().

Returns

Table with all the continuum-fitting results with columns documented in self.init_spec_output().

Return type

astropy.table.Table

Notes

• Consider using cross-correlation to update the redrock redshift.

• We solve for velocity dispersion if solve_vdisp=True or ((SNR_B>3 or SNR_R>3) and REDSHIFT<1).

get_meanage(coeff)

Compute the light-weighted age, given a set of coefficients.

init_phot_output(nobj=1)

Initialize the photometric output data table.

init_spec_output(nobj=1)

Initialize the output data table for this class.

kcorr_and_absmag(data, continuum, coeff, snrmin=2.0)

Computer K-corrections, absolute magnitudes, and a simple stellar mass.

qa_fastphot(data, fastphot, metadata, coadd_type='healpix', outdir=None, outprefix=None)

QA of the best-fitting continuum.

younger_than_universe(redshift)

Return the indices of the SSPs younger than the age of the universe at the given redshift.

class fastspecfit.continuum.ContinuumTools(ssptemplates=None, metallicity='Z0.0190', minwave=None, maxwave=300000.0, sspversion='v1.0', mapdir=None)

Tools for dealing with stellar continua.

Parameters

• metallicity (str, optional, defaults to Z0.0190.) – Stellar metallicity of the SSPs. Currently fixed at solar metallicity, Z=0.0190.

• minwave (float, optional, defaults to None) – Minimum SSP wavelength to read into memory. If None, the minimum available wavelength is used (around 100 Angstrom).

• maxwave (float, optional, defaults to 6e4) – Maximum SSP wavelength to read into memory.

• note:: (..) – Need to document all the attributes.

SSP2data(_sspflux, _sspwave, redshift=0.0, AV=None, vdisp=None, cameras=['b', 'r', 'z'], specwave=None, specres=None, coeff=None, south=True, synthphot=True, debug=False)

Workhorse routine to turn input SSPs into spectra that can be compared to real data.

Redshift, apply the resolution matrix, and resample in wavelength.

Parameters

• redshift –

• specwave –

• specres –

• south –

• photometry? (synthphot - synthesize) –

Return type

Vector or 3-element list of [npix, nmodel] spectra.

Notes

This method does none or more of the following: - redshifting - wavelength resampling - apply dust reddening - apply velocity dispersion broadening - apply the resolution matrix - synthesize photometry

It also naturally handles SSPs which have been precomputed on a grid of reddening or velocity dispersion (and therefore have an additional dimension). However, if the input grid is 3D, it is reshaped to be 2D but then it isn’t reshaped back because of the way the photometry table is organized (bug or feature?).

build_linemask(wave, flux, ivar, redshift=0.0, nsig=7.0)

Generate a mask which identifies pixels impacted by emission lines.

Parameters

• wave (numpy.ndarray [npix]) – Observed-frame wavelength array.

• flux (numpy.ndarray [npix]) – Spectrum corresponding to wave.

• ivar (numpy.ndarray [npix]) – Inverse variance spectrum corresponding to flux.

• redshift (float, optional, defaults to 0.0) – Object redshift.

• nsig (float, optional, defaults to 5.0) – Mask pixels which are within +/-nsig-sigma of each emission line, where sigma is the line-width in km/s.

Returns

Smooth continuum spectrum which can be subtracted from flux in order to create a pure emission-line spectrum.

Return type

smooth numpy.ndarray [npix]

dict with the following keys:

linemask : list linemask_all : list linename : list linepix : list contpix : list

Notes

Code exists to generate some QA but the code is not exposed.

convolve_vdisp(sspflux, vdisp)

Convolve by the velocity dispersion.

Parameters

• sspflux –

• vdisp –

Notes

dust_attenuation(wave, AV, test=False)

Compute the dust attenuation curve A(lambda)/A(V) from Charlot & Fall 2000.

ToDo: add a UV bump and IGM attenuation!

https://gitlab.lam.fr/cigale/cigale/-/blob/master/pcigale/sed_modules/dustatt_powerlaw.py#L42

estimate_linesigmas(wave, flux, ivar, redshift=0.0, png=None, refit=True)

Estimate the velocity width from potentially strong, isolated lines.

static get_dn4000(wave, flam, flam_ivar=None, redshift=None, rest=True)

Compute DN(4000) and, optionally, the inverse variance.

Parameters

• wave –

• flam –

• flam_ivar –

• redshift –

• rest –

Notes

If rest`=``False` then redshift input is required.

Require full wavelength coverage over the definition of the index.

See eq. 11 in Bruzual 1983 (https://articles.adsabs.harvard.edu/pdf/1983ApJ…273..105B) but with the “narrow” definition of Balogh et al. 1999.

static parse_photometry(bands, maggies, lambda_eff, ivarmaggies=None, nanomaggies=True, nsigma=2.0, min_uncertainty=None, debug=False)

Parse input (nano)maggies to various outputs and pack into a table.

Parameters

• erg/s/cm2/A (flam - 10-17) –

• erg/s/cm2/Hz (fnu - 10-17) –

• mag (abmag - AB) –

• maggies (nanomaggies - input maggies are actually 1e-9) –

• limit (nsigma - magnitude) –

Return type

phot - photometric table

Notes

smooth_and_resample(sspflux, sspwave, specwave=None, specres=None)

Given a single template, apply the resolution matrix and resample in wavelength.

Parameters

• sspflux (numpy.ndarray [npix]) – Input (model) spectrum.

• sspwave (numpy.ndarray [npix]) – Wavelength array corresponding to sspflux.

• specwave (numpy.ndarray [noutpix], optional, defaults to None) – Desired output wavelength array, usually that of the object being fitted.

• specres (desispec.resolution.Resolution, optional, defaults to None) – Resolution matrix.

• vdisp (float, optional, defaults to None) – Velocity dispersion broadening factor [km/s].

• pixkms (float, optional, defaults to None) – Pixel size of input spectra [km/s].

Returns

Smoothed and resampled flux at the new resolution and wavelength sampling.

Return type

numpy.ndarray [noutpix]

Notes

This function stands by itself rather than being in a class because we call it with multiprocessing, below.

smooth_continuum(wave, flux, ivar, redshift, medbin=150, smooth_window=50, smooth_step=10, maskkms_uv=3000.0, maskkms_balmer=1000.0, maskkms_narrow=200.0, linemask=None, png=None)

Build a smooth, nonparametric continuum spectrum.

Parameters

• wave (numpy.ndarray [npix]) – Observed-frame wavelength array.

• flux (numpy.ndarray [npix]) – Spectrum corresponding to wave.

• ivar (numpy.ndarray [npix]) – Inverse variance spectrum corresponding to flux.

• redshift (float) – Object redshift.

• medbin (int, optional, defaults to 150 pixels) – Width of the median-smoothing kernel in pixels; a magic number.

• smooth_window (int, optional, defaults to 50 pixels) – Width of the sliding window used to compute the iteratively clipped statistics (mean, median, sigma); a magic number. Note: the nominal extraction width (0.8 A) and observed-frame wavelength range (3600-9800 A) corresponds to pixels that are 66-24 km/s. So smooth_window of 50 corresponds to 3300-1200 km/s, which is conservative for all but the broadest lines. A magic number.

• smooth_step (int, optional, defaults to 10 pixels) – Width of the step size when computing smoothed statistics; a magic number.

• maskkms_uv (float, optional, defaults to 3000 km/s) – Masking width for UV emission lines. Pixels within +/-3*maskkms_uv are masked before median-smoothing.

• maskkms_balmer (float, optional, defaults to 3000 km/s) – Like maskkms_uv but for Balmer lines.

• maskkms_narrow (float, optional, defaults to 300 km/s) – Like maskkms_uv but for narrow, forbidden lines.

• linemask (numpy.ndarray of type bool, optional, defaults to None) – Boolean mask with the same number of pixels as wave where True means a pixel is (possibly) affected by an emission line (specifically a strong line which likely cannot be median-smoothed).

• png (str, optional, defaults to None) – Generate a simple QA plot and write it out to this filename.

Returns

• smooth numpy.ndarray [npix] – Smooth continuum spectrum which can be subtracted from flux in order to create a pure emission-line spectrum.

• smoothsigma numpy.ndarray [npix] – Smooth one-sigma uncertainty spectrum.

fastspecfit.continuum._fnnls_continuum(myargs)

Multiprocessing wrapper for fnnls_continuum.

fastspecfit.continuum.fnnls_continuum(ZZ, xx, flux=None, ivar=None, modelflux=None, get_chi2=False)

Fit a stellar continuum using fNNLS.

Parameters

• ZZ (ndarray) – Array.

• xx (ndarray) – Array.

• flux (ndarray, optional, defaults to None) – Input flux spectrum.

• ivar (ndarray, optional, defaults to None) – Input inverse variance spectrum corresponding to flux.

• modelflux (ndarray, optional, defaults to None) – Input model flux spectrum.

Returns

• bool – Boolean flag indicating whether the non-negative fit did not converge.

• ndarray – Coefficients of the best-fitting spectrum.

• float – Reduced chi-squared of the fit. Only returned if get_chi2=True.

Notes

• This function is a simple wrapper on fastspecfit.fnnls.fnnls(); see the ContinuumFit.fnnls_continuum method for documentation.

• The arguments flux, ivar and modelflux are only used when get_chi2=True.

fastspecfit.emlines

Methods and tools for fitting emission lines.

class fastspecfit.emlines.EMLineFit(nball=10, chi2_default=0.0, minwave=3000.0, maxwave=10000.0, pixkms=10.0, nolegend=False, ssptemplates=None, mapdir=None)

Class to fit an emission-line spectrum.

• https://docs.astropy.org/en/stable/modeling/example-fitting-constraints.html#tied

• https://docs.astropy.org/en/stable/modeling/new-model.html

• https://docs.astropy.org/en/stable/modeling/compound-models.html#parameters

chi2(bestfit, emlinewave, emlineflux, emlineivar, continuum_model=None, return_dof=False)

Compute the reduced chi^2.

emlinemodel_bestfit(specwave, specres, fastspecfit_table, redshift=None)

Wrapper function to get the best-fitting emission-line model from an fastspecfit table (to be used to build QA).

fit(data, continuummodel, smooth_continuum, synthphot=True, maxiter=5000, accuracy=0.01, verbose=False)

Perform the fit minimization / chi2 minimization.

EMLineModel object FC - ContinuumFit object

ToDo: need to take into account the instrumental velocity width when computing integrated fluxes…

init_output(linetable, nobj=1)

Initialize the output data table for this class.

qa_fastspec(data, fastspec, metadata, coadd_type='healpix', wavelims=(3600, 9800), outprefix=None, outdir=None)

QA plot the emission-line spectrum and best-fitting model.

class fastspecfit.emlines.EMLineModel(*args: Any, **kwargs: Any)

Class to model the emission-line spectra.

_emline_spectrum(*lineargs)

Simple wrapper to build an emission-line spectrum.

# build the emission-line model [erg/s/cm2/A, observed frame]

count_free_parameters()

Count the number of free (not tied, not fixed) parameters in the model.

evaluate(emlinewave, *lineargs)

Evaluate the emission-line model.

class fastspecfit.emlines.FastLevMarLSQFitter(*args: Any, **kwargs: Any)

Adopted in part from

https://github.com/astropy/astropy/blob/v4.2.x/astropy/modeling/fitting.py#L1580-L1671

Copyright (c) 2011-2021, Astropy Developers

See https://github.com/astropy/astropy/issues/12089 for details.

fitter_to_model_params(model, fps)

Constructs the full list of model parameters from the fitted and constrained parameters.

model_to_fit_params(model)

Convert a model instance’s parameter array to an array that can be used with a fitter that doesn’t natively support fixed or tied parameters. In particular, it removes fixed/tied parameters from the parameter array. These may be a subset of the model parameters, if some of them are held constant or tied.

fastspecfit.emlines._tie_lines(model)

Tie emission lines together using sensible constrains based on galaxy & QSO physics.

fastspecfit.emlines._tie_nii_amp(model)

[N2] (4–>2): airwave: 6548.0488 vacwave: 6549.8578 emissivity: 2.022e-21 [N2] (4–>3): airwave: 6583.4511 vacwave: 6585.2696 emissivity: 5.949e-21

fastspecfit.emlines._tie_oii_red_amp(model)

[O2] (4–>2): airwave: 7319.9849 vacwave: 7322.0018 emissivity: 3.229e-22 [O2] (4–>3): airwave: 7330.7308 vacwave: 7332.7506 emissivity: 1.692e-22

fastspecfit.emlines._tie_oiii_amp(model)

[O3] (4–>2): airwave: 4958.9097 vacwave: 4960.2937 emissivity: 1.172e-21 [O3] (4–>3): airwave: 5006.8417 vacwave: 5008.2383 emissivity: 3.497e-21

fastspecfit.emlines.read_emlines()

Read the set of emission lines of interest.

fastspecfit.fastspecfit

See sandbox/running-fastspecfit for examples.

fastspecfit.fastspecfit._assign_units_to_columns(fastfit, metadata, Spec, CFit, EMFit=None, fastphot=False)

Assign astropy units to output tables.

fastspecfit.fastspecfit._desiqa_one(args)

Multiprocessing wrapper.

fastspecfit.fastspecfit._fastphot_one(args)

Multiprocessing wrapper.

fastspecfit.fastspecfit._fastspec_one(args)

Multiprocessing wrapper.

fastspecfit.fastspecfit.desiqa_one(CFit, EMFit, data, fastfit, metadata, coadd_type, fastphot=False, outdir=None, outprefix=None)

Multiprocessing wrapper to generate QA for a single object.

fastspecfit.fastspecfit.fastphot(args=None, comm=None)

Main fastphot script.

This script is the engine to model the broadband photometry of one or more DESI objects. It initializes the ContinuumFit class, reads the data, fits each object (with the option of fitting in parallel), and writes out the results as a multi-extension binary FITS table.

Parameters

• args (argparse.Namespace or None) – Required and optional arguments parsed via inputs to the command line.

• comm (mpi4py.MPI.MPI.COMM_WORLD or None) – Intracommunicator used with MPI parallelism.

fastspecfit.fastspecfit.fastphot_one(iobj, data, out, meta, CFit)

Multiprocessing wrapper to run fastphot() on a single object.

fastspecfit.fastspecfit.fastspec(args=None, comm=None)

Main fastspec script.

This script is the engine to model one or more DESI spectra. It initializes the ContinuumFit and EMLineFit classes, reads the data, fits each spectrum (with the option of fitting in parallel), and writes out the results as a multi-extension binary FITS table.

Parameters

• args (argparse.Namespace or None) – Required and optional arguments parsed via inputs to the command line.

• comm (mpi4py.MPI.MPI.COMM_WORLD or None) – Intracommunicator used with MPI parallelism.

fastspecfit.fastspecfit.fastspec_one(iobj, data, out, meta, CFit, EMFit, verbose=False)

Multiprocessing wrapper to run fastspec() on a single object.

fastspecfit.fastspecfit.parse(options=None)

Parse input arguments to fastspec and fastphot scripts.

fastspecfit.fnnls

https://github.com/mikeiovine/fast-nnls.git

This is a Python implementation of the algorithm described in the paper “A Fast Non-Negativity-Constrained Least Squares Algorithm” by Rasmus Bro and Sumen De Jong.

Give a matrix A and a vector y, this algorithm solves argmin_x ||Ax - y||.

At the time of writing this, there are no readily available Python bindings for this algorithm that I know of. scipy.optimize.nnls implements the slower Lawson-Hanson version.

## Usage

A = np.array([[1, 2], [3, 4], [5, 6]]) y = np.array([1, 2, 3]) AtA = A.T.dot(A) Aty = A.T.dot(y) x = fnnls(AtA, Aty)

fastspecfit.fnnls.fnnls(AtA, Aty, epsilon=None, iter_max=None)

Given a matrix A and vector y, find x which minimizes the objective function f(x) = ||Ax - y||^2. This algorithm is similar to the widespread Lawson-Hanson method, but implements the optimizations described in the paper “A Fast Non-Negativity-Constrained Least Squares Algorithm” by Rasmus Bro and Sumen De Jong.

Note that the inputs are not A and y, but are A^T * A and A^T * y

This is to avoid incurring the overhead of computing these products many times in cases where we need to call this routine many times.

Parameters

• AtA (numpy.ndarray) – A^T * A. See above for definitions. If A is an (m x n) matrix, this should be an (n x n) matrix.

• Aty (numpy.ndarray) – A^T * y. See above for definitions. If A is an (m x n) matrix and y is an m dimensional vector, this should be an n dimensional vector.

• epsilon (float) – Anything less than this value is consider 0 in the code. Use this to prevent issues with floating point precision. Defaults to the machine precision for doubles.

• iter_max (int, optional) – Maximum number of inner loop iterations. Defaults to 30 * [number of cols in A] (the same value that is used in the publication this algorithm comes from).

fastspecfit.html

Code to support building HTML visualizations.

fastspecfit.html._build_htmlpage_one(args)

Wrapper function for the multiprocessing.

fastspecfit.html.build_htmlhome(htmldir, fastfit, metadata, tileinfo, coadd_type='cumulative', specprod='denali', htmlhome='index.html', mp=1)

Build the home (index.html) page.

fastspecfit.html.build_htmlpage_one(htmlhome, survey, targetclass, zcolumn, meta, httpfile, pngfile, targiddatafile, nexttargetid, prevtargetid, nexttargidhtmlfile, prevtargidhtmlfile)

Build the web page for a single object.

fastspecfit.io

Tools for reading DESI spectra and reading and writing fastspecfit files.

fastspecfit.io.read_fastspecfit(fastfitfile, rows=None, columns=None)

Read the fitting results.

fastspecfit.io.select(fastfit, metadata, coadd_type, healpixels=None, tiles=None, nights=None)

Optionally trim to a particular healpix or tile and/or night.

fastspecfit.io.write_fastspecfit(out, meta, modelspectra=None, outfile=None, specprod=None, coadd_type=None, fastphot=False)

Write out.

fastspecfit.mpi

MPI tools.

fastspecfit.mpi.backup_logs(logfile)

Move logfile -> logfile.0 or logfile.1 or logfile.n as needed

TODO: make robust against logfile.abc also existing

fastspecfit.mpi.group_redrockfiles(specfiles, maxnodes=256, comm=None, makeqa=False)

Group redrockfiles to balance runtimes

Parameters

specfiles – list of spectra filepaths

Options:

maxnodes: split the spectra into this number of nodes comm: MPI communicator

Returns (groups, ntargets, grouptimes):

• groups: list of lists of indices to specfiles

• list of number of targets per group

• grouptimes: list of expected runtimes for each group

fastspecfit.mpi.merge_fastspecfit(specprod=None, coadd_type=None, survey=None, program=None, healpix=None, tile=None, night=None, outsuffix=None, fastphot=False, specprod_dir=None, outdir_data='.', mergedir=None, supermerge=False, overwrite=False, mp=1)

Merge all the individual catalogs into a single large catalog. Runs only on rank 0.

supermerge - merge previously merged catalogs

fastspecfit.mpi.weighted_partition(weights, n)

Partition weights into n groups with approximately same sum(weights).

Parameters

• weights – array-like weights

• n – number of groups

Returns (groups, groupweights):

groups: list of lists of indices of weights for each group groupweights: sum of weights assigned to each group

fastspecfit.templates.qa

QA for templates

fastspecfit.templates.qa.qa_bpt(targetclass, fastspecfile=None, png=None)

QA of the fastspec emission-line spectra.

fastspecfit.templates.qa.qa_fastspec_emlinespec(targetclass, fastwave=None, fastflux=None, fastivar=None, fastmeta=None, fastspec=None, fastspecfile=None, CFit=None, EMFit=None, ncol=3, nrow=5, pdffile=None)

QA of the fastspec emission-line spectra.

fastspecfit.templates.qa.qa_fastspec_fullspec(targetclass, fastwave=None, fastflux=None, fastivar=None, fastmeta=None, fastspec=None, fastspecfile=None, CFit=None, EMFit=None, ncol=3, nrow=5, photometric_models=False, pdffile=None)

Full-spectrum QA.

photometric_models - use the fits to the broadband continuum

fastspecfit.templates.qa.qa_parent_sample(samplefile, tilefile, targetclass='lrg', specprod='denali', png=None)

Build QA showing how the parent sample was selected.

fastspecfit.templates.qa.qa_photometry(targetclass, samplefile=None, png_obs=None, png_rest=None, png_rest_bins=None)

QA of the observed- and rest-frame photometry.

fastspecfit.templates.qa.qa_photometry_templates(targetclass, samplefile=None, templatefile=None, ntspace=5, png=None)

Compare the color-color tracks of the templates to the data.

fastspecfit.templates.sample

Code for defining samples and reading the data needed to build templates for the various target classes. Called by bin/desi-templates.

fastspecfit.templates.sample.read_fastspecfit(tilestable, fastspecfit_dir=None, specprod='denali', targetclass='lrg')

Read the fastspecfit output for this production.

fastspecfit.templates.sample.read_parent_sample(samplefile)

Read the output of select_parent_sample

fastspecfit.templates.sample.read_tilestable(tilefile)

Read the output of select_tiles

fastspecfit.templates.sample.select_parent_sample(phot, spec, meta, targetclass='lrg', specprod='denali', deltachi2_cut=40, fastphot_chi2cut=100.0, fastspec_chi2cut=3.0, smoothcorr_cut=10, zobj_minmax=None, absmag_minmax=None, color_minmax=None, return_indices=False, verbose=False, png=None, samplefile=None)

High-level sample selection.

fastspecfit.templates.sample.select_tiles(targetclass, remove_vi=False, min_efftime=None, specprod='denali', outfile=None, png=None)

Select tiles to use. Remove low exposure-time tiles and also remove tiles which are being visually inspected.

/global/cfs/cdirs/desi/sv/vi/TruthTables/Blanc/

fastspecfit.templates.templates

Tools for generating stacked spectra and building templates.

fastspecfit.templates.templates._fastspec_onestack(args)

Multiprocessing wrapper.

fastspecfit.templates.templates._rebuild_fastspec_spectrum(args)

Multiprocessing wrapper.

fastspecfit.templates.templates._spectra_allbins_onetile(args)

Multiprocessing wrapper.

fastspecfit.templates.templates._spectra_onebin_onetile(args)

Multiprocessing wrapper.

fastspecfit.templates.templates._stack_onebin(args)

Multiprocessing wrapper.

fastspecfit.templates.templates.build_templates(fastspecfile, mp=1, minwave=None, maxwave=None, templatefile=None)

Build the final templates.

Called by bin/desi-templates.

fastspecfile is the output of fastspecfit_stacks

fastspecfit.templates.templates.fastspec_onestack(fastmeta, fastspec, flux, ivar, meta, wave, CFit, EMFit, qadir, qaprefix)

Fit one stacked spectrum.

fastspecfit.templates.templates.fastspec_to_desidata(fastspec, wave, flux, ivar)

Convenience wrapper to turn spectra in a fastspecfit-compatible dictionary of DESI data.

fastspecfit.templates.templates.fastspecfit_stacks(stackfile, mp=1, qadir=None, qaprefix=None, fastspecfile=None)

Model stacked spectra using fastspecfit.

Called by bin/desi-templates.

stackfile is the output of stack_in_bins.

fastspecfit.templates.templates.iterative_stack(wave, flux2d, ivar2d=None, constant_ivar=False, maxdiff=0.01, maxiter=500, normwave=4500, smooth=None, debug=False, verbose=False)

Iterative stacking algorithm taken from Bovy, Hogg, & Moustakas 2008. https://arxiv.org/pdf/0805.1200.pdf

fastspecfit.templates.templates.quick_stack(wave, flux2d, ivar2d=None, constant_ivar=False)

Simple inverse-variance-weighted stack.

fastspecfit.templates.templates.rebuild_fastspec_spectrum(fastspec, wave, flux, ivar, CFit, EMFit, full_resolution=False, emlines_only=False, normalize_wave=5500.0, normalize_filter=None, normalize_mag=20.0)

Rebuilding a single fastspec model continuum and emission-line spectrum given a fastspec astropy.table.Table.

full_resolution - returns a full-resolution spectrum in the rest-frame!

fastspecfit.templates.templates.remove_undetected_lines(fastspec, linetable=None, snrmin=3.0, oiidoublet=0.73, siidoublet=1.3, niidoublet=2.936, oiiidoublet=2.875, fastmeta=None, devshift=False)

replace weak or undetected emission lines with their upper limits

devshift - remove individual velocity shifts oiidoublet - [OII] 3726/3729 siidoublet - [SII] 6716/6731 niidoublet - [NII] 6584/6548 oiiidoublet - [OIII] 5007/4959

fastspecfit.templates.templates.spectra_allbins_onetile(fastspecdir, targetclass, tile, bins, minperbin=3)

Find the indices of the objects we care about in a given tile across all the bins of properties.

fastspecfit.templates.templates.spectra_in_bins(tilestable, minperbin=3, targetclass='lrg', specprod='denali', fastspecfit_dir=None, minwave=None, maxwave=None, mp=1, normwave=None, fastphot_in_bins=True, verbose=False, stackfile=None)

Select objects in bins of rest-frame properties.

fastphot_in_bins - also stack the fastphot continuum-fitting results

fastspecfit.templates.templates.spectra_onebin_onetile(fastspecdir, targetclass, tile, bins, minperbin=3, CFit=None, continuumwave=None, index=None, return_index=False)

For Find (and, optionally, read) all the spectra in a given tile and bin of properties.

fastspecfit.templates.templates.stack_in_bins(sample, data, templatewave, mp=1, normwave=4500.0, continuumwave=None, stackfile=None)

Stack spectra in bins given the output of a spectra_in_bins run.

See bin/desi-templates for how to generate the required input.

This routine is basically obsolete – we now use spectra_in_bins directly because there were memory issues passing around the grids of spectra before making the stacks.

fastspecfit.templates.templates.stack_onebin(flux2d, ivar2d, templatewave, normwave, cflux2d, continuumwave)

Build one stack.

fastspecfit.templates.templates.write_binned_stacks(outfile, wave, flux, ivar, metadata=None, cwave=None, cflux=None, fastspec=None, fastmeta=None)

Write out the stacked spectra.

fastspecfit.templates.templates.write_templates(outfile, wave, flux, metadata, weights=None, empca=False)

Write out the final templates

fastspecfit.util

General utilities.

class fastspecfit.util.ZWarningMask

Mask bit definitions for zwarning.

WARNING on the warnings: not all of these are implemented yet.

#- TODO: Consider using something like desispec.maskbits to provide a more #- convenient wrapper class (probably copy it here; don’t make a dependency) #- That class as-is would bring in a yaml dependency.

fastspecfit.util._trapz_rebin(x, y, edges, results)

Numba-friendly version of trapezoidal rebinning

See redrock.rebin.trapz_rebin() for input descriptions. results is pre-allocated array of length len(edges)-1 to keep results

fastspecfit.util.air2vac(airwave)

http://www.astro.uu.se/valdwiki/Air-to-vacuum%20conversion

fastspecfit.util.centers2edges(centers)

Convert bin centers to bin edges, guessing at what you probably meant

Parameters

centers (array) – bin centers,

Returns

bin edges, lenth = len(centers) + 1

Return type

array

fastspecfit.util.find_minima(x)

Return indices of local minima of x, including edges.

The indices are sorted small to large.

Note

this is somewhat conservative in the case of repeated values: find_minima([1,1,1,2,2,2]) -> [0,1,2,4,5]

Parameters

x (array-like) – The data array.

Returns

The indices.

Return type

(array)

fastspecfit.util.ivar2var(ivar, clip=0.001, sigma=False, allmasked_ok=False)

Safely convert an inverse variance to a variance. Note that we clip at 1e-3 by default, not zero.

fastspecfit.util.minfit(x, y)

Fits y = y0 + ((x-x0)/xerr)**2

See redrock.zwarning.ZWarningMask.BAD_MINFIT for zwarn failure flags

Parameters

• x (array) – x values.

• y (array) – y values.

Returns

(x0, xerr, y0, zwarn) where zwarn=0 is good fit.

Return type

(tuple)

fastspecfit.util.trapz_rebin(x, y, xnew=None, edges=None)

Rebin y(x) flux density using trapezoidal integration between bin edges

Notes

y is interpreted as a density, as is the output, e.g.

>>> x = np.arange(10)
>>> y = np.ones(10)
>>> trapz_rebin(x, y, edges=[0,2,4,6,8])  #- density still 1, not 2
array([ 1.,  1.,  1.,  1.])

Parameters

• x (array) – input x values.

• y (array) – input y values.

• edges (array) – (optional) new bin edges.

Returns

integrated results with len(results) = len(edges)-1

Return type

array

Raises

ValueError – if edges are outside the range of x or if len(x) != len(y)