"""
===============
Gaussian Fitter
===============
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
^^^^^^^^^^
"""
import numpy as np
import warnings
from . import model
from . import fitter
from ...specwarnings import PyspeckitWarning
[docs]def 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?
"""
if width == 0:
return np.nan
elif width < 0:
warnings.warn("Negative width in Gaussian: {0}.".format(width),
PyspeckitWarning)
xarr = np.array(xarr) # make sure xarr is no longer a spectroscopic axis
model = amplitude*np.exp(-(xarr-dx)**2/(2.0*width**2))
if normalized:
return model / (np.sqrt(2*np.pi) * width**2)
else:
return model
def gaussian_fwhm(sigma):
return np.sqrt(8*np.log(2)) * sigma
[docs]def gaussian_integral(amplitude, sigma):
""" Integral of a Gaussian """
return amplitude * np.sqrt(2*np.pi*sigma**2)
def _integral_modelpars(modelpars=None):
""" light wrapper to match requirements for model.analytic_integral """
amplitude = modelpars[0]
sigma = modelpars[2]
return gaussian_integral(amplitude,sigma)
[docs]def gaussian_fitter():
"""
Generator for Gaussian fitter class
"""
myclass = model.SpectralModel(gaussian, 3,
parnames=['amplitude','shift','width'],
parlimited=[(False,False),(False,False),(True,False)],
parlimits=[(0,0), (0,0), (0,0)],
shortvarnames=('A',r'\Delta x',r'\sigma'),
centroid_par='shift',
fwhm_func=gaussian_fwhm,
fwhm_pars=['width'],
integral_func=_integral_modelpars,
)
myclass.__name__ = "gaussian"
return myclass
[docs]def gaussian_vheight_fitter():
"""
Generator for Gaussian fitter class
"""
vhg = fitter.vheightmodel(gaussian)
myclass = model.SpectralModel(vhg, 4,
parnames=['height','amplitude','shift','width'],
parlimited=[(False,False),(False,False),(False,False),(True,False)],
parlimits=[(0,0),(0,0), (0,0), (0,0)],
shortvarnames=('B','A',r'\Delta x',r'\sigma'),
centroid_par='shift',
fwhm_func=gaussian_fwhm,
fwhm_pars=['width'],
)
myclass.__name__ = "vheightgaussian"
return myclass
