kcorrect.fitter

Classes

Fitter

Nonnegative SED fitting object

Module Contents

class Fitter(responses=None, templates=None, redshift_range=[0.0, 1.0], nredshift=2000, abcorrect=False)

Bases: object

Nonnegative SED fitting object

Parameters:

• abcorrect (bool) – correct maggies to AB (default False)

• responses (list of str) – names of responses to use

• templates (kcorrect.template.Template object) – templates to use

• redshift_range (list of np.float32) – minimum and maximum redshifts (default [0., 1.])

• nredshift (int or np.int32) – number of redshifts in interpolation grid

abcorrect

correct maggies to AB

Type:

bool

Amatrix

interpolator to produce A matrix (set to None until set_Amatrix called)

Type:

scipy.interpolate.interp1d object

responses

names of responses to use

Type:

list of str

nredshift

number of redshifts in interpolation grid

Type:

int or np.int32

redshift_range

minimum and maximum redshifts (default [0., 1.])

Type:

list of np.float32

redshifts

redshifts in grid

Type:

ndarray of np.float32

templates

templates to use

Type:

kcorrect.template.Template object

abcorrect = False

Amatrix = None

responses = None

templates = None

nredshift = 2000

redshift_range

redshifts

_interpolate_Amatrix(redshifts=None, A=None)

Interpolate the A matrix to an interpolator

_calc_Amatrix(responses=None)

Create an A matrix and return it (don’t set attribute)

to_ab(maggies=None, ivar=None)

Convert input maggies to AB

Parameters:

• maggies (ndarray of np.float32) – array of fluxes in standard SDSS system

• ivar (ndarray of np.float32) – inverse variances in standard SDSS system (optional)

Returns:

• ab_maggies (ndarray of np.float32) – array of fluxes converted to AB

• ab_ivar (ndarray of np.float32) – inverse variances converted to AB (if ivar input)

Notes

This method just returns maggies and/or ivar unchanged, as for this object we expect AB maggies on input.

fit_coeffs() calls this on its inputs.

set_Amatrix()

Set Amatrix, interpolator for the design matrix

_fit_coeffs(redshift=None, maggies=None, ivar=None, mc=0)

Fit coefficients to single object

Parameters:

• redshift (np.float32) – redshift

• maggies (ndarray of np.float32) – fluxes of each band in AB maggies

• ivar (ndarray of np.float32) – inverse variance of each band

• mc (int) – if greater than 0, generate Monte Carlo of coefficients

Returns:

• coeffs (ndarray of np.float32) – [ntemplates] coefficients for each template

• coeffs_mc (ndarray of np.float32) – [ntemplates, mc] coefficients for each MC for each template

• maggies_mc (ndarray of np.float32) – [len(maggies), mc] maggies for each MC

Notes

This recasts the A and b matrices as np.float64 before sending them to scipy.optimize.nnls(). This is necessitated by a change in the algorithm for NNLS in SciPy 1.12.0 and later, which is apparently less numerically robust than the original algorithm.

_process_inputs(redshift=None, maggies=None, ivar=None, coeffs=None)

Returns whether input should be an array, and casts everything right

Parameters:

• redshift (a quantity or ndarray) – input redshift defining whether we have an array or scalar

• maggies (a quantity or ndarray, or None) – input maggies

• ivar (a quantity or ndarray, or None) – input ivar

• coeffs (a quantity or ndarray, or None) – input coeffs

Returns:

• array (bool) – True if it is an array

• n (int) – number of redshifts (1 if not an array)

• redshift (np.float32 or [n] ndarray thereof) – redshift to use

• maggies (ndarray of np.float32 or None) – AB maggies to use

• ivar (ndarray of np.float32 or None) – ivar to use

• coeffs (ndarray of np.float32 or None) – coeffs to use

Notes

Uses redshift to determine whether we should treat the calculation as an array or scalar. Then checks the appropriate sizes (based on the number of responses and the number of seds in the object). If maggies, ivar, or coeffs are None, then the corresponding output is None.

Applies this class’s to_ab() function on maggies and ivar to convert to return AB maggies.

fit_coeffs(redshift=None, maggies=None, ivar=None, mc=0)

Fit coefficients

Parameters:

• redshift (np.float32 or ndarray of np.float32) – redshift(s)

• maggies (ndarray of np.float32) – fluxes of each band in AB maggies

• ivar (ndarray of np.float32) – inverse variance of each band

• mc (int) – if greater than 1, generate Monte Carlo of coefficients

Returns:

• coeffs (ndarray of np.float32) – coefficients for each template

• coeffs_mc (ndarray of np.float32) – [ntemplates, mc] coefficients for each MC for each template

• maggies_mc (ndarray of np.float32) – [nbands, mc] Monte Carlo maggies

Notes

maggies are assumed to be Galactic-extinction corrected already.

Calls this class’s to_ab() method on input maggies.

If redshift is an array, even with just one element, coeffs is returned as an [nredshift, ntemplate] array.

Otherwise coeffs is returned as an [ntemplate] array.

Occasionally the optimizer will report “NNLS quitting on iteration count.” This indicates that the default number of iterations for scipy.optimize.nnls was not enough. Under these conditions, this code tries a much larger number of iterations. If that still fails, you will receive a traceback.

_reconstruct(Amatrix=None, redshift=None, coeffs=None, band_shift=0.0)

Reconstruct maggies associated with coefficients

Parameters:

• Amatrix (scipy.interpolate.interp1d) – interpolator to use for Amatrix

• redshift (np.float32) – redshift

• coeffs (ndarray of np.float32) – coefficients

• band_shift (np.float32) – blueshift to apply to reconstructed bandpasses

Returns:

maggies – maggies in each band

Return type:

ndarray of np.float32

Notes

Amatrix should be an interpolator over redshift that returns an array that is number of responses by number of templates.

reconstruct(redshift=None, coeffs=None, band_shift=0.0)

Reconstruct AB maggies associated with coefficients

Parameters:

• redshift (np.float32 or ndarray of np.float32) – redshift

• coeffs (ndarray of np.float32) – coefficients

• band_shift (np.float32) – blueshift to apply to reconstructed bandpasses

Returns:

maggies – AB maggies in each band

Return type:

ndarray of np.float32

Notes

Returns AB maggies, but note that if to_ab() is non-trivial, these may not be directly comparable to the input maggies.