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java.lang.Objectjhplot.stat.StatShape
public class StatShape
Shape identification based on a linear regression It calculates eccentricities in the transverse and longitudinal directions, as well as the size of the objects. Used used for identification of statistical patterns. Data points can have weights.
Constructor Summary | |
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StatShape(double[] x,
double[] y,
double[] w,
int n)
Perform a linear regression analysis with weights and prepare for a shape identification. |
Method Summary | |
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void |
doc()
Show online documentation. |
double[] |
getCenters(int k)
Get the centers in non-quadrant method. |
double |
getChi2()
Get Chi2 of this fit |
double[] |
getCovariance()
Get covariance matrix. |
P1D |
getData()
Get data back used for fitting (can be inverted in Y and Y for best results). |
F1D |
getFitFunction()
Get a fit function after the linear regression (major axis). |
F1D |
getFitFunctionPerp()
Get a fit function perpendicular to the linear regression line (minor axis). |
F1D[] |
getFitFunctionQuadrants()
Get linear functions after rotation of major and minor axes by 45 deg to define the quadrants. |
double[] |
getFitParameters()
Get fit parameters, the intercept (0) and the slope (1) |
double[] |
getFitParametersPerp()
Get fit parameters of a function perpendicular (minor) axis to the linear regression line the intercept (0) and the slope (1) |
double[] |
getFitParametersRotate()
Parameters after rotation of major and minor axes by 45deg. |
double[] |
getMeans()
Get weighted means in X and Y |
double[] |
getSummary()
Return all shape parameters |
void |
process(int choice)
Process event. |
Methods inherited from class java.lang.Object |
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equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
Constructor Detail |
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public StatShape(double[] x, double[] y, double[] w, int n)
x
- array in xy
- array in yw
- weight of the data pointn
- total number of pointsMethod Detail |
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public P1D getData()
public double getChi2()
public double[] getCovariance()
public void process(int choice)
option
- option =0 use weighted linear regression and weighted means in quadrants
=1 use unweighted linear regression and weighted means in quadrants
=2 use unweighted calculations for everythingpublic double[] getFitParameters()
public F1D getFitFunction()
public F1D getFitFunctionPerp()
public double[] getFitParametersPerp()
public double[] getFitParametersRotate()
public F1D[] getFitFunctionQuadrants()
public double[] getMeans()
public double[] getCenters(int k)
k
- current center centers (1,2,3,4)
public double[] getSummary()
public void doc()
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