Gaussian Model

The Gaussian model implements a Gaussian function and wraps it in the generic fitter tools.

The simplest and most useful model.

Until 12/23/2011, gaussian fitting used the complicated and somewhat bloated gaussfitter.py code. Now, this is a great example of how to make your own model! Just make a function like gaussian and plug it into the SpectralModel class.

Module API

pyspeckit.spectrum.models.inherited_gaussfitter.gaussian(xarr, amplitude, dx, width, return_components=False, normalized=False, return_hyperfine_components=False)

Returns a 1-dimensional gaussian of form A*np.exp(-(x-dx)**2/(2*w**2))

Area is sqrt(2*pi*sigma^2)*amplitude - i.e., this is NOT a normalized gaussian, unless normalized=True in which case A = Area

Parameters

xarr : np.ndarray

array of x values

amplitude : float

Amplitude of the Gaussian, i.e. its peak value, unless normalized=True then A is the area of the gaussian

dx : float

Center or “shift” of the gaussian

width : float

Width of the gaussian (sigma)

return_components : bool

dummy variable; return_components does nothing but is required by all fitters

return_hyperfine_components : bool

dummy variable; does nothing but is required by all fitters

normalized : bool

Return a normalized Gaussian?

pyspeckit.spectrum.models.inherited_gaussfitter.gaussian_fitter()

Generator for Gaussian fitter class

pyspeckit.spectrum.models.inherited_gaussfitter.gaussian_integral(amplitude, sigma)

Integral of a Gaussian

pyspeckit.spectrum.models.inherited_gaussfitter.gaussian_vheight_fitter()

Generator for Gaussian fitter class

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