jhplot.fit
Class Landau
java.lang.Object
hep.aida.ref.event.AIDAObservable
hep.aida.ref.ManagedObject
hep.aida.ref.function.AbstractIFunction
jhplot.fit.Landau
- All Implemented Interfaces:
- hep.aida.dev.IDevManagedObject, hep.aida.IFunction, hep.aida.IManagedObject, hep.aida.IModelFunction, Connectable, FunctionDispatcher, Cloneable
public class Landau
- extends AbstractIFunction
The function represents the Landau distribution.
This class represents a Landau distribution, as
approximated by the Moyal formula
\[ Moyal(\lambda) = \frac{\exp{-0.5(\lambda+\exp{-\lambda})}}{\sqrt{2\pi}} \]
See J.E. Moyal, Theory of ionization fluctuations, Phil. Mag. 46 (1955) 263.
Note that this analytical approximation is too low in the tail.
In order to allow for a fit, we define
\[ \lambda = \frac{x - m}{s} \]
with x the dataset variable.
From Goddard GLAST ACD team (Fortran version)
Method Summary |
double |
value(double[] v)
Provide value for your function here. |
Methods inherited from class hep.aida.ref.function.AbstractIFunction |
addFunctionListener, annotation, clone, codeletString, dimension, excludeNormalizationAll, gradient, includeNormalizationAll, indexOfParameter, isEqual, isNormalized, normalizationParameter, normalizationRange, normalize, numberOfParameters, parameter, parameterGradient, parameterNames, parameters, providesGradient, providesNormalization, providesParameterGradient, removeFunctionListener, setCodeletString, setParameter, setParameters, setTitle, title, variableName, variableNames |
Landau
public Landau()
Landau
public Landau(String title)
Landau
public Landau(String[] variableNames,
String[] parameterNames)
value
public double value(double[] v)
- Description copied from class:
AbstractIFunction
- Provide value for your function here. Something like:
return p[0]+p[1]*v[0]+p[2]*v[0]*v[0];
- Specified by:
value
in interface hep.aida.IFunction
- Specified by:
value
in class AbstractIFunction
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