gaussfitter

Latest version available at <http://code.google.com/p/agpy/source/browse/trunk/agpy/gaussfitter.py>

agpy.gaussfitter.gaussfit(data, err=None, params=(), autoderiv=True, return_all=False, circle=False, fixed=array([False, False, False, False, False, False, False], dtype=bool), limitedmin=[False, False, False, False, True, True, True], limitedmax=[False, False, False, False, False, False, True], usemoment=array([], dtype=bool), minpars=array([0, 0, 0, 0, 0, 0, 0]), maxpars=[0, 0, 0, 0, 0, 0, 360], rotate=1, vheight=1, quiet=True, returnmp=False, returnfitimage=False, **kwargs)

Gaussian fitter with the ability to fit a variety of different forms of 2-dimensional gaussian.

Input Parameters:

data - 2-dimensional data array err=None - error array with same size as data array params=[] - initial input parameters for Gaussian function.

(height, amplitude, x, y, width_x, width_y, rota) if not input, these will be determined from the moments of the system, assuming no rotation

autoderiv=1 - use the autoderiv provided in the lmder.f function (the

alternative is to us an analytic derivative with lmdif.f: this method is less robust)

return_all=0 - Default is to return only the Gaussian parameters.

1 - fit params, fit error

returnfitimage - returns (best fit params,best fit image) returnmp - returns the full mpfit struct circle=0 - default is an elliptical gaussian (different x, y widths),

but can reduce the input by one parameter if it’s a circular gaussian

rotate=1 - default allows rotation of the gaussian ellipse. Can remove

last parameter by setting rotate=0. numpy.expects angle in DEGREES

vheight=1 - default allows a variable height-above-zero, i.e. an

additive constant for the Gaussian function. Can remove first parameter by setting this to 0

usemoment - can choose which parameters to use a moment estimation for.

Other parameters will be taken from params. Needs to be a boolean array.

Output:

Default output is a set of Gaussian parameters with the same shape as

the input parameters

Can also output the covariance matrix, ‘infodict’ that contains a lot

more detail about the fit (see scipy.optimize.leastsq), and a message from leastsq telling what the exit status of the fitting routine was

Warning: Does NOT necessarily output a rotation angle between 0 and 360 degrees.

agpy.gaussfitter.moments(data, circle, rotate, vheight, estimator=<function median at 0x102a48b18>, **kwargs)

Returns (height, amplitude, x, y, width_x, width_y, rotation angle) the gaussian parameters of a 2D distribution by calculating its moments. Depending on the input parameters, will only output a subset of the above.

If using masked arrays, pass estimator=numpy.ma.median

agpy.gaussfitter.multigaussfit(xax, data, ngauss=1, err=None, params=[1, 0, 1], fixed=[False, False, False], limitedmin=[False, False, True], limitedmax=[False, False, False], minpars=[0, 0, 0], maxpars=[0, 0, 0], quiet=True, shh=True, veryverbose=False)

An improvement on onedgaussfit. Lets you fit multiple gaussians.

Inputs:

xax - x axis data - y axis ngauss - How many gaussians to fit? Default 1 (this could supersede onedgaussfit) err - error corresponding to data

These parameters need to have length = 3*ngauss. If ngauss > 1 and length = 3, they will be replicated ngauss times, otherwise they will be reset to defaults:

params - Fit parameters: [amplitude, offset, width] * ngauss

If len(params) % 3 == 0, ngauss will be set to len(params) / 3

fixed - Is parameter fixed? limitedmin/minpars - set lower limits on each parameter (default: width>0) limitedmax/maxpars - set upper limits on each parameter

quiet - should MPFIT output each iteration? shh - output final parameters?

Returns:

Fit parameters Model Fit errors chi2

agpy.gaussfitter.n_gaussian(pars=None, a=None, dx=None, sigma=None)

Returns a function that sums over N gaussians, where N is the length of a,dx,sigma OR N = len(pars) / 3

The background “height” is assumed to be zero (you must “baseline” your spectrum before fitting)

pars - a list with len(pars) = 3n, assuming a,dx,sigma repeated dx - offset (velocity center) values sigma - line widths a - amplitudes

agpy.gaussfitter.onedgaussfit(xax, data, err=None, params=[0, 1, 0, 1], fixed=[False, False, False, False], limitedmin=[False, False, False, True], limitedmax=[False, False, False, False], minpars=[0, 0, 0, 0], maxpars=[0, 0, 0, 0], quiet=True, shh=True, veryverbose=False, vheight=True, negamp=False, usemoments=False)

Inputs:

xax - x axis data - y axis err - error corresponding to data

params - Fit parameters: Height of background, Amplitude, Shift, Width fixed - Is parameter fixed? limitedmin/minpars - set lower limits on each parameter (default: width>0) limitedmax/maxpars - set upper limits on each parameter quiet - should MPFIT output each iteration? shh - output final parameters? usemoments - replace default parameters with moments

Returns:

Fit parameters Model Fit errors chi2

agpy.gaussfitter.onedgaussian(x, H, A, dx, w)

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

agpy.gaussfitter.onedmoments(Xax, data, vheight=True, estimator=<function median at 0x102a48b18>, negamp=None, veryverbose=False, **kwargs)

Returns (height, amplitude, x, width_x) the gaussian parameters of a 1D distribution by calculating its moments. Depending on the input parameters, will only output a subset of the above.

If using masked arrays, pass estimator=numpy.ma.median ‘estimator’ is used to measure the background level (height)

negamp can be used to force the peak negative (True), positive (False), or it will be “autodetected” (negamp=None)

agpy.gaussfitter.twodgaussian(inpars, circle=False, rotate=True, vheight=True, shape=None)

Returns a 2d gaussian function of the form: x’ = numpy.cos(rota) * x - numpy.sin(rota) * y y’ = numpy.sin(rota) * x + numpy.cos(rota) * y (rota should be in degrees) g = b + a * numpy.exp ( - ( ((x-center_x)/width_x)**2 + ((y-center_y)/width_y)**2 ) / 2 )

inpars = [b,a,center_x,center_y,width_x,width_y,rota]

(b is background height, a is peak amplitude)

where x and y are the input parameters of the returned function, and all other parameters are specified by this function

However, the above values are passed by list. The list should be: inpars = (height,amplitude,center_x,center_y,width_x,width_y,rota)

You can choose to ignore / neglect some of the above input parameters

unumpy.sing the following options: circle=0 - default is an elliptical gaussian (different x, y

widths), but can reduce the input by one parameter if it’s a circular gaussian

rotate=1 - default allows rotation of the gaussian ellipse. Can

remove last parameter by setting rotate=0

vheight=1 - default allows a variable height-above-zero, i.e. an

additive constant for the Gaussian function. Can remove first parameter by setting this to 0

shape=None - if shape is set (to a 2-parameter list) then returns

an image with the gaussian defined by inpars