Using SplinePolynomial for data smoothing
Source code name: "spline_smoothing.py"
Programming language: Python
Topic: Statistics/Smoothing
DMelt Version 1.4. Last modified: 09/14/2019. License: Free
http://datamelt.org/code/cache/spline_smoothing_1134.py
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# This example runs cubic spline, calculates chi2/ndf and stops when chi2/ndf<1
from java.awt import Color
from jhplot import *
from jhplot.stat import Interpolator
import time

nbins,min,max=100,-1000,1000
h1 = H1D("Data",nbins, min, max)
h1.fillGauss(100000, 0, 900)  # mean=0, sigma=100 
h1.fillGauss(5000, 200, 100) #  mean=200, sigma=100
p1=P1D(h1,0,0)

def getResult(k1,p1,rho,nbins,min,max):
    s1=k1.interpolateCubicSpline(rho,2) 
    p2=P1D()
    fit=s1.getSplinePolynomials()
    h=(max-min)/float(nbins); chi2=0
    for i in range(nbins):
           z1 = min + i * h; 
           z2=s1.evaluate(z1)
           delta=((p1.getY(i)-z2)*(p1.getY(i)-z2) ) / (p1.getErr(i)*p1.getErr(i))
           chi2= chi2+delta
           p2.add(z1,z2)
    chi2ndf= chi2/float(nbins)
    if (chi2ndf<1): return None
    p2.setTitle( "CubicSpline ρ="+str(rho)+ " χ^{2}/ndf ="+str(int(chi2ndf)))  
    p2.setStyle("l")
    p2.setColor(Color.red)
    return p2

c1 = HPlot("Spline 3rd order"); c1.visible()
c1.setRange(min-100,max+100,400,1500)
k1=Interpolator(p1)
rho=0.0000000001
for i in range(20):
      rho=rho*2
      p2=getResult(k1,p1,rho,nbins,min,max)
      if (p2 != None):
               c1.clearData()
               c1.draw([p1,p2])
      time.sleep(0.3)