FreeShapeModel

FreeShapeModel = class FreeShapeModel(LinearModel)

FreeShapeModel(npix, copy=None, shape=None, nconvolve=0, center=0.5, xlo=0.0, xhi=None, **kwargs) Pixelated Model. f( x:p ) = p( expanded ) where p is a array of amplitudes of the same size as the input x divided by the number of pixels per bin ( ppb ). When ppb > 1, each p has a shape which is used to fill the pixels of the bin. Initially the shape is a top-hat, which can be autoconvolved. By default ppb = 5. The parameters are initialized at {0}. Although this is a LinearModel it will not work very well with the ( linear ) Fitter. It will be a very ill-posed problem. Using NestedSampler its exponential prior will ensure that all parameters are kept positive. Attributes ---------- npix : int Number of pixels in result. Is also npar. xlo : float ( default 0 ) Lowest value in xdata xhi : float ( default npix ) Highest value in xdata xlo and xhi define the valid domain of the model. All input data must be: xlo <= xdata <= xhi shape : Kernel shape of convolving function center : float (between 0..1) position of the center of shape with respect to the pixels Attributes from Model --------------------- npchain, parameters, stdevs, xUnit, yUnit Attributes from FixedModel -------------------------- npmax, fixed, parlist, mlist Attributes from BaseModel -------------------------- npbase, ndim, priors, posIndex, nonZero, tiny, deltaP, parNames Examples -------- >>> nn = 100 >>> fsm = FreeShapeModel( nn, nconvolve=4, xlo=-1.0, xhi=4.0 ) >>> print( fsm.shape ) Author Do Kester

Method resolution order:: FreeShapeModel; LinearModel; Model; FixedModel; BaseModel; builtins.object

Constructor:

TBCbaseDerivative( xdata, params ): Returns the derivative of the model function df/dx. Parameters ---------- xdata : array_like value at which to calculate the result params : array_like values for the parameters

Methods defined here:

FreeShapeModel( npix, copy=None, shape=None, nconvolve=0, center=0.5,
xlo=0.0, xhi=None, **kwargs): Free Shape model with npix pixels. The number of parameters equals the number of pixels Parameters ---------- npix : int number of pixels = npar copy : FreeShapeModel model to be copied shape : None or Kernel None : Use Tophat(), convolved nconvolve times. Kernel : use the kernel as shape; nconvolve does not apply. nconvolve : int number of (auto)convolutions on Tophat center : float (between 0..1) positions where the pixels are centered. default: 0.5 -> pixels run from k to k+1 xlo : float ( default 0.0 ) lowest value in xdata xhi : float ( default np ) highest value in xdata

baseName()

baseParameterUnit( k ): Return the unit of the indicated parameter. Parameters --------- k : int parameter number.

basePartial( xdata, params, parlist=None ): Returns the partial derivative of the model function to each of the parameters. Parameters ---------- xdata : array_like value at which to calculate the result params : array_like values for the parameters

baseResult( xdata, params ): Returns the result of the model function. Parameters ---------- xdata : array_like value at which to calculate the result params : array_like values for the parameters

checkDomain( xdata ): Check for all data inside domain defined by (xlo - range, xhi + range). range = self.shape.range Parameters ---------- xdata : array_like value at which to calculate the result Raises ------ ValueError when outside domain.

copy(): Copy method.

Methods inherited from Model:

Overloaded operators and aliases

Other methods

Methods inherited from FixedModel:

Methods inherited from BaseModel: