PDFs and the basic models

Ostap provides set of useful wrapper and helper class that drastically simplify the construction and manipulations with RooAbsPdf-objects.

E.g. consider the simplest case - creation of the Gaussian PDF using the standard way the standard way:

x     = ROOT.RooRealVar ('x'    ,'x'   ,2,3) 
mean  = ROOT.RooRealVar ('mean' ,'mean' ,3.100,3.080,3.120) 
sigma = ROOT.RooRealVar ('sigma','sigma',0.015,0.010,0.025) 
bare  = ROOT.RooGaussian('Gauss','Gaussian', x , mean , sigma ) ## <--- HERE

In ostap it can be done in a bit simpler way 

gauss = Gauss_pdf  ( 'Gauss' , 
                        xvar  = ( 2 , 3 ) , 
                      mean  = ( 3.100 , 3.080 , 3.120 ) ,
                      sigma = ( 0.015 , 0.010 , 0.025 ) )
gauss.draw() ## and one can immediately visualize the model

How to define parameter?Click to expand

For all models, all known parameter are accessible (and documented) as python property

gauss = ...
help(gauss.xvar)
print gauss.sigma 
help(gauss.mean)

There are many predefined models, accesible via ostap.fitting.models module:

import ostap.fitting.models as Models
help(Models)

Base class PDF

All pstap-based fit models and PDFs (directy or indirectly) inherit from python base class PDF, that provides great additional functionality, in particular the methods fitTo and draw that simplfy the fitting procedure itself and visualzation of the results:

The method fitTo

gauss   = Gauss_pdf ( ... ) 
dataset = ....
result , frame = gauss.fitTo ( dataset , silent = True , reFit = 2 ) 
print 'FitResults: %s' % result

All the native RooFit commands can be specified as optional arguments, as well as many commands specific for ostap, e.g. reFit=2 above means in case of fit failure, try to refit it (up to 2 times), and the meaning of silent=True is obvious.

The method draw

gauss   = Gauss_pdf ( ... ) 
dataset = ....
result , frame = gauss.fitTo ( dataset , silent = True , reFit = 2 ) 
print 'FitResults: %s' % result 
frame = gauss.draw ( dataset , nbins = 100 )

Fitting and vizualisation can be combined:

gauss   = Gauss_pdf ( ... ) 
dataset = ....
result , frame = gauss.fitTo ( dataset , draw  = True , nbins = 100 ) ## draw it after the fit

Access to the underlying RooAbsPdf object

The access to the underlying bare RooAbsPdf-object can be done (if needed) via the propety pdf 

gauss = Gauss_pdf ( ... ) 
root_pdf  = gauss.pdf

Other methods

PDF class is equipped with many other useful methods:

  • fitHisto: The method fitTo can be blindly applied not only to RooDataSet-objects, but also to the histograms:
    histo = ...
r, f   = gauss.fitTo ( histo , draw = True )
    However the dedicated method fitHisto sometimes could be more usefu 
    histo = ...
gauss.fitHisto ( histo , draw = True )
  • draw_nll: vizualize NLL-scans and LL-profiles 
    r , f = gauss.fitTo ( dataset , draw = False )
nll     , f1 = gauss.draw_nll ( 'sigma' ,  dataset )                  ## NLL
profile , f2 = gauss.draw_nll ( 'sigma' ,  dataset , profile = True ) ## PROFILE
    • generate : tiny but useful wrapper for RooAbsPdf::generate
    • minmax : make the estimates for the minimal and maximal values for the PDF. For some models it is done analytically or semianalitycally, for remainig models it is done using random shoots. 
      mn,mx = gauss.minmax( 500000 )
    • __call__ : it allows to use PDF as simple function
      gauss = ...
print gauss( 3.090 ), gauss( 3.100 ), gauss( 3.110 )
    • Several statistical functions. For some models analytical orsemianalitycal calculations are used, for remnig models numerical estimations are performed using scipy
    • rms : rms for the distribution
    • fwhm : full width at half maximum
    • fwhm : full width at half maximum
    • moment : the moment of the distribution
    • central_moment : the central moment of the distribution
    • skewness : skewness for the distribution
    • kurtosis : kurtosis for the distribution
    • mode : the mode for the distribution
    • median : median value for the distribution
    • get_mean : mean value for the distribution
    • cl_symm : symmetric confidence interval
    • cl_asymm : asymmetric confidence interval
    • quantile : quantile value for the distribution
    • integral : integral for the distribution
    • derivative : derivative of the PDF at the given point

Convolution

Ostap provides helper class that simplify construction of fit models taking into accotun resolution functions:

pdf = ...
cnv_pdf = Convolution_pdf ( 'Cnv            ' , 
                             pdf = pdf        , 
                             resolution = ... )

As resolution one can specify

  1. Any resolutuon model (RooAbsPdf)
  2. simple number s, in this case the gaussian resolution model with sigma = s will be used
  3. Any RooAbsReal objetct, it will be used as sigma for gaussian resoltuion model

There are several optional flags

  • useFFT=True : use Fast-Fourier-Transform or plain numerical convolution ?
  • nbins=100000 : sampling for Fast-Fourier-Transform
  • buffer=0.25 : buffer size for Fast-Fourier-Transform, argument for setBufferFraction call
  • nsigmas=6 : window size for plain numeric convolution, the argument for setConvolutionWindow call

Generic Wrapper Generic1D_pdf

The bare RooAbsPdf could be easily converted to ostap-form using the generic wrapper Generic1D_pdf:

bare = ROOT.RooGaussian('Gauss','Gaussian', x , mean , sigma )
gauss = Generic1D_pdf ( pdf = bare , xvar = x ) 
gauss.draw() ## one can immediately use the full power of ostap-PDF

In a similar way there are generic wrappers for 2D and 3D-models:

bare2D = ... 
bare3D = ... 
ostap_2d =  Generic2D_pdf ( pdf = bare2D , xvar = x ,  xvar = y ) 
ostap_3d =  Generic2D_pdf ( pdf = bare3D , xvar = x ,  xvar = y , zvar = z )

1D-models

There are many predefined models, accessible via ostap.fitting.models module:

import ostap.fitting.models as Models
help(Models)

Generic backrgound models

Polynomial models

Here the list of the most useful polynomial models:

  • PolyPos_pdf : positive (non-negative) polynomial
  • PolyEven_pdf : positibe (non-negative) symmetric polynomial: p(x)= p(2*x0-x), where x0=0.5*(xmin+xmax)
  • Monotonic_pdf : positive (non-negative) polynomial with fixed sign of the first derivative: posynomial either non-decreasing or non-increasing
  • Convex_pdf : positive (non-negative) polynomial with fixed signs of the first (non-decreasing or non-increasing) and second (convex or concave) derivatives
  • ConvexOnly_pdf : positive (non-negative) polynomial with fixed sign of the second (convex or concave) derivative

Phasespace-based models

Here the list of the most useful phasespace-based models:

  • PS2_pdf : 2-body phase space (no parameters)
  • PSLeft_pdf : Low edge of N-body phase space
  • PSRight_pdf : High edge of L-body phase space from N-body decays
  • PSNL_pdf : approximation for L-body phase space from N-body decays
  • PS23L_pdf : 2-body phase space from 3-body decays with orbital momenta

Polynomial-based models

  • Bkg_pdf : The exponential function, modulated by the positive polynomial. In practice it is the most useful function to describe the combinatorial background
  • PSPol_pdf : L-body phase space from N-body decays modulated by a positive polynomial
  • Sigmoid_pdf : sigmoid function (atanh) modulated by the positive polynomial
  • TwoExpoPoly_pdf : difference of two exponents, modulated by the positive polynomial

Spline-based models

The models, based on B-splines :

  • PSpline_pdf : positive (non-negative) spline
  • MSpline_pdf : positive (non-negative) monothonic (non-decreasing or non-increasing) spline
  • CSpline_pdf : positive (non-negative) monothonic (non-decreasing or non-inclreasing) convex or concave spline
  • CPSpline_pdf : positive (non-negative) convex or concave spline

Generic signal models

The signal-like models (peaks):

'Gauss_pdf'              , ## simple     Gauss
'CrystalBall_pdf'        , ## Crystal-ball function
'CrystalBallRS_pdf'      , ## right-side Crystal-ball function
'CB2_pdf'                , ## double-sided Crystal Ball function    
'Needham_pdf'            , ## Needham function for J/psi or Y fits 
'Apolonios_pdf'          , ## Apolonios function         
'Apolonios2_pdf'         , ## Apolonios function         
'BifurcatedGauss_pdf'    , ## bifurcated Gauss
'DoubleGauss_pdf'        , ## double Gauss
'GenGaussV1_pdf'         , ## generalized normal v1  
'GenGaussV2_pdf'         , ## generalized normal v2 
'SkewGauss_pdf'          , ## skewed gaussian (temporarily removed)
'Bukin_pdf'              , ## generic Bukin PDF: skewed gaussian with exponential tails
'StudentT_pdf'           , ## Student-T function 
'BifurcatedStudentT_pdf' , ## bifurcated Student-T function
'SinhAsinh_pdf'          , ## "Sinh-arcsinh distributions". Biometrika 96 (4): 761
'JohnsonSU_pdf'          , ## JonhsonSU-distribution 
'Atlas_pdf'              , ## modified gaussian with exponenital tails 
'Slash_pdf'              , ## symmetric peakk wot very heavy tails 
'RaisingCosine_pdf'      , ## Raising  Cosine distribution
'QGaussian_pdf'          , ## Q-gaussian distribution
'AsymmetricLaplace_pdf'  , ## asymmetric laplace 
'Sech_pdf'               , ## hyperboilic secant  (inverse-cosh) 
'Logistic_pdf'           , ## Logistic aka "sech-squared"   
#
## pdfs for "wide" peaks, to be used with care - phase space corrections are large!
# 
'BreitWigner_pdf'      , ## (relativistic) 2-body Breit-Wigner
'Flatte_pdf'           , ## Flatte-function  (pipi)
'Flatte2_pdf'          , ## Flatte-function  (KK) 
'LASS_pdf'             , ## kappa-pole
'Bugg_pdf'             , ## sigma-pole
'Swanson_pdf'          , ## Swanson's S-wave cusp 
##
'Voigt_pdf'            , ## Voigt-profile
'PseudoVoigt_pdf'      , ## PseudoVoigt-profile
'BW23L_pdf'            , ## BW23L

2D and 3D-cases

For 2D and 3D cases there are base classes PDF2 and PDF3 that in turn inhetic from PDF and gets all the nice functionality. Of course several new method specific for 2D and 3D-cases are added and th ebehaviosu of some 1D-specific methods is fixed.

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