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This section deals with advanced techniques of fitting data using data in arbitrary dimension, arbitrary function. In this section we will discuss how to perform more interactive fit using scripting.

Let us perform a fit of data points using a function defined by a Python script:

from java.lang.Math import * from jhplot import * class PowerLawFunc(): # function describing a power law def __init__(self, p0,p1): self.p0=p0; self.p1=p1; def valueOf(self, x): return self.p0*pow(x,self.p1); # power law def getPlot(self,min,max): # plotting part f=P1D("p0*x^p1") step=(max-min)/100.0 for i in range(100): x=min+step*i y=self.valueOf(x) f.add(x,y) return f

As you can see, this is a simple power-law function of one variable “x” defined in the method
“valueOf”. One interesting method of this function is
getPlot(min,max) that allows to plot this function between min and max.
This method returns `jhplot.P1D`

plottable on `jhplot.HPlot`

Now let us perform a fit with this function using Minuit minimisation program and the Chi2 method.

where “y” is the data point, “e” is the expectation, is “variance” related to the measurement error for “y”

Let us make a Jython script which:

- Performs Chi2 fit with the above function
- Prints evolution of chi2 for each iteration and print final chi2
- Calculates Minuit convergence steps and change Migrad/Minuit strategy in case of a failed fit
- Calculates calculates Migrad parameter correlations
- Calculate uncertainty on the fit parameters
- Plot data and the minimized function

For this task, we will use `MnMigrad`

class.
The script that does all the above using this class is shown bellow:

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The fit result of the data is shown below, where the blue result is the fit:

The fit parameters are:

p0=1.14152 +/- 0.276695 p1=0.704415 +/- 0.0431544

Here is the complete output of the script:

Click to show the output of the script

Click to show the output of the script

start Migrad Chi2= 470.358172895 Chi2= 151.491535555 Chi2= 152.13739461 Chi2= 151.491535555 Chi2= 150.199077966 Chi2= 146.963922788 Chi2= 137.231555692 Chi2= 108.020960351 Chi2= 29.5665676492 Chi2= 224.944087841 Chi2= 11.1675109612 Chi2= 3.09063261351 Chi2= 2.83286407423 Chi2= 2.83089798953 Chi2= 2.60969185498 Chi2= 2.58038208008 Chi2= 2.37208952261 Chi2= 2.37208949274 Chi2= 2.37209280753 Minuit did successfully converge. # of function calls: 77 minimum function value: 2.37209 minimum edm: 2.72672e-09 minimum internal state vector: LAVector parameters: 1.14152 0.704415 minimum internal covariance matrix: LASymMatrix parameters: 0.0765603 -0.0117485 -0.0117485 0.00186230 # ext. || name || type || value || error +/- 0 || p0 || free || 1.14152 || 0.276695 1 || p1 || free || 0.704415 || 0.0431544 MnUserCovariance: 0.0765603 -0.0117485 -0.0117485 0.00186230 MnUserCovariance parameter correlations: 1.00000 -0.983907 -0.983907 1.00000 MnGlobalCorrelationCoeff: 0.983907 0.983907

Alternatively, one can fit data using the Maximum Likelihood method. Again, we will use
`MnMigrad`

class. This approach is shown in this script:

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Note the difference: we replaced “PowerLawChi2FCN” that implements a Chi2 minimization function with “PowerLawLogLikeFCN” which calculates the sum of . The Minuit minimizes this function (note the minus in the front, which means that the likelihood sum should be maximal). As before, the script prints the value of the maximum likelihood.

The examples above can be written in Java.

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The `MnMigrad`

class can also perform fit
in multiple dimensions.
Now we will show how to fit data in a fully controlled way in several dimensions. As example, we will consider 2D data fitted with 2D function.

The result of this script is shown below. The data are plotted on the left canvas, while the function with fitted parameters after the chi2 minimisation is on the right figure.

Similarly, one can implement the likelihood fit as it was discussed inn 1D case.

— *Sergei Chekanov 2014/01/19 11:10*