Wrappers

These are wrappers to simplify some of the more complicated (and even some of the simpler) functions in PySpecKit

Cube Fitting

pyspeckit.wrappers.cube_fit.cube_fit(cubefilename, outfilename, errfilename=None, scale_keyword=None, vheight=False, verbose=False, signal_cut=3, verbose_level=2, clobber=True, **kwargs)

Light-weight wrapper for cube fitting

Takes a cube and error map (error will be computed naively if not given) and computes moments then fits for each spectrum in the cube. It then saves the fitted parameters to a reasonably descriptive output file whose header will look like

PLANE1  = 'amplitude'
PLANE2  = 'velocity'
PLANE3  = 'sigma'
PLANE4  = 'err_amplitude'
PLANE5  = 'err_velocity'
PLANE6  = 'err_sigma'
PLANE7  = 'integral'
PLANE8  = 'integral_error'
CDELT3  = 1
CTYPE3  = 'FITPAR'
CRVAL3  = 0
CRPIX3  = 1

Parameters:

errfilename [ None | string name of .fits file ]

A two-dimensional error map to use for computing signal-to-noise cuts

scale_keyword [ None | Char ]

Keyword to pass to the data cube loader - multiplies cube by the number indexed by this header kwarg if it exists. e.g., if your cube is in T_A units and you want T_A*

vheight [ bool ]

Is there a background to be fit? Used in moment computation

verbose [ bool ] verbose_level [ int ]

How loud will the fitting procedure be? Passed to momenteach and fiteach

signal_cut [ float ]

Signal-to-Noise ratio minimum. Spectra with a peak below this S/N ratio will not be fit and will be left blank in the output fit parameter cube

clobber [ bool ]

Overwrite parameter .fits cube if it exists?

kwargs are passed to pyspeckit.Spectrum.specfit

Simple Gaussian Fitter

pyspeckit.wrappers.fit_gaussians_to_simple_spectra.fit_gaussians_to_simple_spectra(filename, units='km/s', doplot=True, baseline=True, plotresiduals=False, figuresavename=None, croprange=None, savename=None, **kwargs)

As stated in the name title, will fit Gaussians to simple spectra!

kwargs will be passed to specfit

figuresavename [ None | string ]

After fitting, save the figure to this filename if specified

croprange [ list of 2 floats ]

Crop the spectrum to (min,max) in the specified units

savename [ None | string ]

After fitting, save the spectrum to this filename

Note that this wrapper can be used from the command line:

python fit_gaussians_to_simple_spectra.py spectrum.fits

NH3 fitter wrapper

Wrapper to fit ammonia spectra. Generates a reasonable guess at the position and velocity using a gaussian fit

pyspeckit.wrappers.fitnh3.BigSpectrum_to_NH3dict(sp, vrange=None)

A rather complicated way to make the spdicts above given a spectrum...

pyspeckit.wrappers.fitnh3.fitnh3tkin(input_dict, dobaseline=True, baselinekwargs={}, crop=False, guessline='twotwo', tex=15, tkin=20, column=15.0, fortho=0.66000000000000003, tau=None, thin=False, quiet=False, doplot=True, fignum=1, guessfignum=2, smooth=False, scale_keyword=None, rebase=False, npeaks=1, guesses=None, **kwargs)

Given a dictionary of filenames and lines, fit them together e.g. {‘oneone’:’G000.000+00.000_nh3_11.fits’}

pyspeckit.wrappers.fitnh3.plot_nh3(spdict, spectra, fignum=1, show_components=False, residfignum=None, **plotkwargs)

Plot the results from a multi-nh3 fit

pyspeckit.wrappers.fitnh3.plotter_override(sp, vrange=None, **kwargs)

Do plot_nh3 with syntax similar to plotter()

N2H+ fitter wrapper

Wrapper to fit N2H+ using RADEX models. This is meant to be used from the command line, e.g.:

python n2hp_wrapper.py file.fits

and therefore has no independently defined functions.

In place of the actual contents of N2H+ fitter, here are the modules used to make the wrapper

model.SpectralModel()

A wrapper class for a spectra model. Includes internal functions to generate multi-component models, annotations, integrals, and individual components. The declaration can be complex, since you should name individual variables, set limits on them, set the units the fit will be performed in, and set the annotations to be used. Check out some of the hyperfine codes (hcn, n2hp) for examples.

static n2hp.n2hp_radex(xarr, density=4, column=13, xoff_v=0.0, width=1.0, grid_vwidth=1.0, grid_vwidth_scale=False, texgrid=None, taugrid=None, hdr=None, path_to_texgrid='', path_to_taugrid='', temperature_gridnumber=3, debug=False, verbose=False, **kwargs)

Use a grid of RADEX-computed models to make a model line spectrum

The RADEX models have to be available somewhere. OR they can be passed as arrays. If as arrays, the form should be: texgrid = ((minfreq1,maxfreq1,texgrid1),(minfreq2,maxfreq2,texgrid2))

xarr must be a SpectroscopicAxis instance xoff_v, width are both in km/s

grid_vwidth is the velocity assumed when computing the grid in km/s

this is important because tau = modeltau / width (see, e.g., Draine 2011 textbook pgs 219-230)

grid_vwidth_scale is True or False: False for LVG, True for Sphere