Documentation API of the 'jhplot.stat.StatShape' Java class

Class StatShape

  • public class StatShapeextends Object
    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 Detail

      • StatShape

        public StatShape(double[] x,                 double[] y,                 double[] w,                 int n)
        Perform a linear regression analysis with weights and prepare for a shape identification.
        x - array in x
        y - array in y
        w - weight of the data point
        n - total number of points
    • Method Detail

      • getData

        public P1D getData()
        Get data back used for fitting (can be inverted in Y and Y for best results).
      • getChi2

        public double getChi2()
        Get Chi2 of this fit
      • getCovariance

        public double[] getCovariance()
        Get covariance matrix. 0: cov_00, 1: cov_11, 2:cov_01
      • process

        public void process(int choice)
        Process event. Perform all calculations of eccentricities, axis lengths, centers, etc.
        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 everything
      • getFitParameters

        public double[] getFitParameters()
        Get fit parameters, the intercept (0) and the slope (1)
        array with intercept (index 0) and the slop (index 1)
      • getFitFunction

        public F1D getFitFunction()
        Get a fit function after the linear regression (major axis).
        fit function
      • getFitFunctionPerp

        public F1D getFitFunctionPerp()
        Get a fit function perpendicular to the linear regression line (minor axis).
        fit function
      • getFitParametersPerp

        public double[] getFitParametersPerp()
        Get fit parameters of a function perpendicular (minor) axis to the linear regression line the intercept (0) and the slope (1)
        array with intercept (index 0) and the slop (index 1)
      • getFitParametersRotate

        public double[] getFitParametersRotate()
        Parameters after rotation of major and minor axes by 45deg. To locate weighted centers in 4 quadrants first get new slopes
      • getFitFunctionQuadrants

        public F1D[] getFitFunctionQuadrants()
        Get linear functions after rotation of major and minor axes by 45 deg to define the quadrants.
        fit functions
      • getMeans

        public double[] getMeans()
        Get weighted means in X and Y
        array with weighted mean in X (index 0) and Y (index 1)
      • getCenters

        public double[] getCenters(int k)
        Get the centers in non-quadrant method. There should be 4 cluster centers separated by the major and minor vectors
        k - current center centers (1,2,3,4)
        2D array with X and Y positions
      • getSummary

        public double[] getSummary()
        Return all shape parameters
        array with shape characteristics p[0] = majorLength; p[1] = minorLength; p[2] = 1 - minorLength / majorLength; p[3] = majorLength1; p[4] = majorLength2; p[5] = 1 - majorLength1 / majorLength2; p[6] = minorLength1; p[7] = minorLength2; p[8] = 1 - minorLength1 / minorLength2; p[9] = GlobalMajorLength; p[10] = GlobalMinorLength; p[11] = majorLength_meth2; p[12] = minorLength_meth2; p[13] = 1 - minorLength_meth2 / majorLength_meth2; p[14] = nq_majorLength; p[15] = nq_minorLength; p[16] = 1 - nq_minorLength / nq_majorLength; p[17] = nq_majorLength_meth2; p[18] = nq_minorLength_meth2; p[19] = 1 - nq_minorLength_meth2 / nq_majorLength_meth2; p[20] = 1 - nq_majorLength1 / nq_majorLength2; p[21] = 1 - nq_minorLength1 / nq_minorLength2; p[22] = Fmax;
      • doc

        public void doc()
        Show online documentation.

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