Decorations
Ostap decorates many ROOT.RooFit
classes, adding more convinient methods to them.
RooArgList
and RooArgSet
All these classes have got set of additional python-like methods for iteration, extension, addition, elemtn access checking the content etc...
Also several methods to provide more coherent interfaces (e.g. add
vs Add
) are added.
RooAbsData and RooDataSet
These methods also have got the extended interface with many useful methods and operators, like
e.g. concatenation of datasets a+b
and merging them a*c
.
RooDataSet
class also has go many methods, that are similar to those of ROOT.TTree
, in particular project
and draw
:
dataset = ...
dataset.draw('mass','pt>1')
histo = ...
dataset.project ( histo , 'mass', 'pt>1' )
Many other methonds like statVar
, sumVar
, statCov
, vminmax
are also the same as for ROOT.TTree
, see above.
s1 = dataset.statVar ('eff')
s2 = dataset.sumVar ('eff')
r = dataset.statCov ('eff','pt')
mn,mx = dataset.vminmax ('eff')
RooFitResult
The class RooFitResult
get many decorations that allow to access fit results
result = ...
par1 = result.params() ## get all floating parameters
par2 = result.params( float_only = False ) ## all parameters
a,v = result.param ( 'a' ) ## par by name
a,v = result.param ( a ) ## par by RooFit object itself
p = result.a ## par as attribute
for par in result : print par ## iteration
for name,par in result.iteritems() : print par ## iteration
print result.cov ( 'a' , 'b' ) ## get the covariance submatrix
print result.corr ( 'a' , 'b' ) ## get the correlation coefficient
Also the simple math with fiting parameters is supported
result = ...
s = result.sum ('S','B' ) ## S+B
d = result.divide ('S','B' ) ## S/B
s = result.subtract ('B','B1') ## B-B1
m = result.multiply ('A','B' ) ## A*B
f = result.fraction ('S','B' ) ## S/(S+B)
RooRealVar & friends
Few simple operations are added to simplify the calculations with RooRealVar
objects:
x = ROOT.RooRealVar( ... )
x + 10
x - 10
x * 10
x / 10
10 + x
10 - x
10 * x
10 / x
x += 2
x -= 2
x *= 2
x /= 2
x ** 3