Model Fitting

class pyspeckit.spectrum.fitters.Specfit(Spectrum, Registry=None)

Bases: pyspeckit.spectrum.interactive.Interactive

EQW(plot=False, plotcolor='g', annotate=False, alpha=0.5, loc='lower left')

Returns the equivalent width (integral of “baseline” or “continuum” minus the spectrum) over the selected range

annotate(loc='upper right', labelspacing=0.25, markerscale=0.01, borderpad=0.10000000000000001, handlelength=0.10000000000000001, handletextpad=0.10000000000000001, frameon=False, **kwargs)

Add a legend to the plot showing the fitted parameters

clearlegend() will remove the legend

kwargs passed to legend

button3action(event, debug=False)

Disconnect the interactiveness Perform the fit (or die trying) Hide the guesses

clear(legend=True, components=True)

Remove the fitted model from the plot

Also removes the legend by default

clear_all_connections(debug=False)

Prevent overlapping interactive sessions

clear_highlights()

copy(parent=None)

Create a copy of the spectral fit - includes copies of the _full_model, the registry, the fitter, parinfo, modelpars, modelerrs, model, npeaks

[ parent ]

A spectroscopic axis instance that is the parent of the specfit instance. This needs to be specified at some point, but defaults to None to prevent overwriting a previous plot.

crop(x1pix, x2pix)

When spectrum.crop is called, this must be too

downsample(factor)

Downsample the model spectrum (and the spectofit spectra) This should only be done when Spectrum.smooth is called

event_manager(event, debug=False)

Decide what to do given input (click, keypress, etc.)

firstclick_guess()

Initialize self.guesses

firstclick_selection(include_all=False)

Initialize the include/exclude mask

fullsizemodel()

If the gaussian was fit to a sub-region of the spectrum, expand it (with zeros) to fill the spectrum.

guesspeakwidth(event, debug=False)

Interactively guess the peak height and width from user input

Width is assumed to be half-width-half-max

highlight_fitregion(drawstyle='steps-mid', color='g', clear_highlights=True, **kwargs)

Re-highlight the fitted region

kwargs are passed to matplotlib.plot

integral(direct=False, threshold='auto', integration_limits=[], return_error=False, **kwargs)

Return the integral of the fitted spectrum

if direct=True, return the integral of the spectrum over a range defined by the threshold or integration limits if defined

note that integration_limits will operate directly on the DATA, which means that if you’ve baselined without subtract=True, the baseline will be included in the integral

if return_error is set, the error computed by sigma = sqrt(sum(sigma_i^2)) * dx will be returned as well

moments(**kwargs)

Return the moments

see moments

multifit(fittype=None, renormalize='auto', annotate=None, show_components=None, verbose=True, **kwargs)

Fit multiple gaussians (or other profiles)

fittype - What function will be fit? fittype must have been Registryed in the

singlefitters dict. Uses default (‘gaussian’) if not specified

renormalize - if ‘auto’ or True, will attempt to rescale small data (<1e-9) to be

closer to 1 (scales by the median) so that the fit converges better

peakbgfit(usemoments=True, annotate=None, vheight=True, height=0, negamp=None, fittype=None, renormalize='auto', show_components=None, debug=False, nsigcut_moments=None, **kwargs)

Fit a single peak (plus a background)

usemoments - The initial guess will be set by the fitter’s ‘moments’ function

(this overrides ‘guesses’)

annotate - Make a legend? vheight - Fit a (constant) background as well as a peak? height - initial guess for background negamp - If True, assumes amplitude is negative. If False, assumes positive. If

None, can be either.

fittype - What function will be fit? fittype must have been Registryed in the

singlefitters dict

renormalize - if ‘auto’ or True, will attempt to rescale small data (<1e-9) to be

closer to 1 (scales by the median) so that the fit converges better

nsigcut_moments - pass to moment guesser; can do a sigma cut for moment guessing

plot_fit(annotate=None, show_components=None, composite_fit_color='red', component_fit_color='blue', lw=0.5, composite_lw=0.75, component_lw=0.75, component_kwargs={}, **kwargs)

Plot the fit. Must have fitted something before calling this!

It will be automatically called whenever a spectrum is fit (assuming an axis for plotting exists)

kwargs are passed to the fitter’s components attribute

plotresiduals(fig=2, axis=None, clear=True, **kwargs)

Plot residuals of the fit. Specify a figure or axis; defaults to figure(2).

kwargs are passed to matplotlib plot

print_fit(print_baseline=True, **kwargs)

Print the best-fit parameters to the command line

register_fitter(*args, **kwargs)

Register a model fitter

Register a fitter function.

Required Arguments:

name: [ string ]

The fit function name.

function: [ function ]

The fitter function. Single-fitters should take npars + 1 input parameters, where the +1 is for a 0th order baseline fit. They should accept an X-axis and data and standard fitting-function inputs (see, e.g., gaussfitter). Multi-fitters should take N * npars, but should also operate on X-axis and data arguments.

npars: [ int ]

How many parameters does the function being fit accept?

Optional Keyword Arguments:

multisingle: [ ‘multi’ | ‘single’ ]

Is the function a single-function fitter (with a background), or does it allow N copies of the fitting function?

override: [ True | False ]

Whether to override any existing type if already present.

key: [ char ]

Key to select the fitter in interactive mode

savefit()

Save the fit parameters from a Gaussian fit to the FITS header *THESE SHOULD BE WRITTEN FOR EACH TYPE OF MODEL TO BE FIT*

selectregion(xmin=None, xmax=None, xtype='wcs', highlight=False, fit_plotted_area=True, reset=False, verbose=False, debug=False, **kwargs)

Pick a fitting region in either WCS units or pixel units

selectregion_interactive(event, mark_include=True, debug=False, **kwargs)

select regions for baseline fitting

seterrspec(usestd=None, useresiduals=True)

Simple wrapper function to set the error spectrum; will either use the input spectrum or determine the error using the RMS of the residuals, depending on whether the residuals exist.

setfitspec()

Set the spectrum that will be fit. This is primarily to remove NANs from consideration: if you simply remove the data from both the X-axis and the Y-axis, it will not be considered for the fit, and a linear X-axis is not needed for fitting.

However, it may be possible to do this using masked arrays instead of setting errors to be 1e10....

shift_pars(frame=None)

Shift the velocity / wavelength / frequency of the fitted parameters into a different frame

Right now this only takes care of redshift and only if redshift is defined. It should be extended to do other things later

start_interactive(debug=False, LoudDebug=False, print_message=True, clear_all_connections=True, **kwargs)