NonParametricModel

class paintbox.NonParametricModel(wave, templates, names=None)[source] [edit on github]

Bases: paintbox.sed.PaintboxBase

Weighted linear combination of SED models.

This class allows the combination of a set of templates based on different weights.

Attributes
parnames: list

Name of the templates.

Parameters
wave: ndarray, Quantity

Common wavelenght array of all templates.

templates: 2D ndarray

SED models with dimensions (N, len(wave)), where N=number of templates.

names: list

Name of the templates. Defaults to [temp, …, tempN]

Attributes Summary

parnames

List with names of the parameters of the model.

Methods Summary

__call__(theta)

Returns the dot product of a vector theta with the templates.

constrain_duplicates()

Constrain the parameters with the same name to have the same value during calling.

fix(fixed_vals)

Fix a set of parameters in parnames using a dictionary.

gradient(theta)

Gradient of the dot product with weights theta.

plot(theta[, ax, plottype])

Plot the model for a given set of parameters.

Attributes Documentation

parnames

List with names of the parameters of the model.

Methods Documentation

__call__(theta)[source] [edit on github]

Returns the dot product of a vector theta with the templates.

Parameters
theta: ndarray

Vector with weights of the templates.

Returns

Dot product of theta with templates.

constrain_duplicates() [edit on github]

Constrain the parameters with the same name to have the same value during calling.

fix(fixed_vals) [edit on github]

Fix a set of parameters in parnames using a dictionary.

gradient(theta)[source] [edit on github]

Gradient of the dot product with weights theta.

This routine returns simply the templates, but it has an argument theta only to keep calls consistently across different SED components.

plot(theta, ax=None, plottype=None, **kwargs) [edit on github]

Plot the model for a given set of parameters.