Statistic Class Reference

Public Member Functions
	__init__ (self, nTrueSig, nSig, nBg)
	nSig (self)
	nTrueSig (self)
	nBg (self)
	nTotal (self)
	purity (self)
	efficiency (self)
	purityError (self)
	efficiencyError (self)
	calcStandardDeviation (self, k, n)
	__str__ (self)
	__add__ (self, a)
	__radd__ (self, a)

Protected Attributes
int	_nTrueSig = nTrueSig
	the number of true signal particles
int	_nSig = nSig
	the number of reconstructed signal candidates
	_nBg = nBg
	the number of reconstructed background candidates

Detailed Description

This class provides the efficiency, purity and other quantities for a
given number of true signal candidates, signal candidates and background candidates

Definition at line 49 of file monitoring.py.

Constructor & Destructor Documentation

__init__()

__init__	(		self,
			nTrueSig,
			nSig,
			nBg )

Create a new Statistic object
@param nTrueSig the number of true signal particles
@param nSig the number of reconstructed signal candidates
@param nBg the number of reconstructed background candidates

Definition at line 55 of file monitoring.py.

    def __init__(self, nTrueSig, nSig, nBg):
        """
        Create a new Statistic object
        @param nTrueSig the number of true signal particles
        @param nSig the number of reconstructed signal candidates
        @param nBg the number of reconstructed background candidates
        """
        
        self._nTrueSig = nTrueSig
        
        self._nSig = nSig
        
        self._nBg = nBg
 

Member Function Documentation

__add__()

__add__	(		self,
			a )

 Adds two Statistics objects and returns a new object. 

Definition at line 145 of file monitoring.py.

    def __add__(self, a):
        """ Adds two Statistics objects and returns a new object. """
        # \cond false positive doxygen warning
        return Statistic(self.nTrueSig, self.nSig + a.nSig, self.nBg + a.nBg)
        # \endcond
 

__radd__()

__radd__	(		self,
			a )

Returns a new Statistic object if the current one is added to zero.
Necessary to apply sum-function to Statistic objects.

Definition at line 151 of file monitoring.py.

    def __radd__(self, a):
        """
        Returns a new Statistic object if the current one is added to zero.
        Necessary to apply sum-function to Statistic objects.
        """
        if a != 0:
            return NotImplemented
        # \cond false positive doxygen warning
        return Statistic(self.nTrueSig, self.nSig, self.nBg)
        # \endcond
 
 

__str__()

__str__

(

self

)

 Returns a string representation of a Statistic object. 

Definition at line 138 of file monitoring.py.

    def __str__(self):
        """ Returns a string representation of a Statistic object. """
        o = f"nTrueSig {self.nTrueSig}    nSig {self.nSig}    nBg {self.nBg}\n"
        o += f"Efficiency {self.efficiency:.3f} ({self.efficiencyError:.3f})\n"
        o += f"Purity {self.purity:.3f} ({self.purityError:.3f})\n"
        return o
 

calcStandardDeviation()

calcStandardDeviation	(		self,
			k,
			n )

 Helper method to calculate the standard deviation for efficiencies. 

Definition at line 129 of file monitoring.py.

    def calcStandardDeviation(self, k, n):
        """ Helper method to calculate the standard deviation for efficiencies. """
        k = float(k)
        n = float(n)
        variance = (k + 1) * (k + 2) / ((n + 2) * (n + 3)) - (k + 1) ** 2 / ((n + 2) ** 2)
        if variance <= 0:
            return 0.0
        return math.sqrt(variance)
 

efficiency()

efficiency

(

self

)

 Returns the efficiency of the reconstructed signal candidates with respect to the number of true signal particles. 

Definition at line 101 of file monitoring.py.

    def efficiency(self):
        """ Returns the efficiency of the reconstructed signal candidates with respect to the number of true signal particles. """
        if self._nSig == 0:
            return 0.0
        if self._nTrueSig == 0:
            return float('inf')
        return self._nSig / float(self._nTrueSig)
 

efficiencyError()

efficiencyError

(

self

)

Returns the uncertainty of the efficiency.
For an efficiency eps = self._nSig/self._nTrueSig, this function calculates the
standard deviation according to http://arxiv.org/abs/physics/0701199 .

Definition at line 119 of file monitoring.py.

    def efficiencyError(self):
        """
        Returns the uncertainty of the efficiency.
        For an efficiency eps = self._nSig/self._nTrueSig, this function calculates the
        standard deviation according to http://arxiv.org/abs/physics/0701199 .
        """
        if self._nTrueSig == 0:
            return float('inf')
        return self.calcStandardDeviation(self._nSig, self._nTrueSig)
 

nBg()

nBg

(

self

)

 Returns the number of reconstructed background candidates. 

Definition at line 80 of file monitoring.py.

    def nBg(self):
        """ Returns the number of reconstructed background candidates. """
        return self._nBg
 

nSig()

nSig

(

self

)

 Returns the number of reconstructed signal candidates. 

Definition at line 70 of file monitoring.py.

    def nSig(self):
        """ Returns the number of reconstructed signal candidates. """
        return self._nSig
 

nTotal()

nTotal

(

self

)

 Returns total number of reconstructed candidates. 

Definition at line 85 of file monitoring.py.

    def nTotal(self):
        """ Returns total number of reconstructed candidates. """
        return self._nSig + self._nBg
 

nTrueSig()

nTrueSig

(

self

)

 Returns the number of reconstructed true signal candidates. 

Definition at line 75 of file monitoring.py.

    def nTrueSig(self):
        """ Returns the number of reconstructed true signal candidates. """
        return self._nTrueSig
 

purity()

purity

(

self

)

 Returns the purity of the reconstructed candidates. 

Definition at line 90 of file monitoring.py.

    def purity(self):
        """ Returns the purity of the reconstructed candidates. """
        if self._nSig == 0:
            return 0.0
        # \cond false positive doxygen warning
        if self.nTotal == 0:
            return 0.0
        return self._nSig / float(self.nTotal)
        # \endcond
 

purityError()

purityError

(

self

)

 Returns the uncertainty of the purity. 

Definition at line 110 of file monitoring.py.

    def purityError(self):
        """ Returns the uncertainty of the purity. """
        # \cond false positive doxygen warning
        if self.nTotal == 0:
            return 0.0
        return self.calcStandardDeviation(self._nSig, self.nTotal)
        # \endcond
 

Member Data Documentation

_nBg

_nBg = nBg

protected

the number of reconstructed background candidates

Definition at line 67 of file monitoring.py.

_nSig

_nSig = nSig

protected

the number of reconstructed signal candidates

Definition at line 65 of file monitoring.py.

_nTrueSig

_nTrueSig = nTrueSig

protected

the number of true signal particles

Definition at line 63 of file monitoring.py.

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The documentation for this class was generated from the following file:

• analysis/scripts/fei/monitoring.py