Package flanagan.plot

Examples of flanagan.plot.PlotGraph

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            }
            if(super.deadTime!=0.0D)for(int i=0; i<n; i++)cdata[0][i] += super.deadTime;


            // Plot
            PlotGraph pg = new PlotGraph(cdata);

            pg.setGraphTitle("Ramp (a.t^n) Input Transient:   ramp gradient (a) = "+rampGradient + " ramp order (n) = " + rampOrder);
            pg.setGraphTitle2(this.getName());
            pg.setXaxisLegend("Time");
            pg.setXaxisUnitsName("s");
            pg.setYaxisLegend("Output");
            pg.setPoint(0);
            pg.plot();
        }
    }
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            // Estimates of the errors in the best fit mean and sd
            this.probPlotStats(0, gaussianParam);

            // Create instance of PlotGraph
            PlotGraph pg = new PlotGraph(data);
            int[] points = {4, 0};
            pg.setPoint(points);
            int[] lines = {0, 3};
            pg.setLine(lines);
            pg.setXaxisLegend("Gaussian Order Statistic Medians");
            pg.setYaxisLegend("Ordered Data Values");
            pg.setGraphTitle("Gaussian probability plot:   gradient = " + Fmath.truncate(this.gaussianLine[1], 4) + ", intercept = "  +  Fmath.truncate(this.gaussianLine[0], 4) + ",  R = " + Fmath.truncate(this.gaussianCorrCoeff, 4));
            pg.setGraphTitle2("  mu = " + Fmath.truncate(this.gaussianParam[0], 4) + ", sigma = "  +  Fmath.truncate(this.gaussianParam[1], 4));

            // Plot
            pg.plot();

            this.gaussianDone = true;
        }
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            // Estimates of the errors in the best fit mu, sigma and gamma
            this.probPlotStats(1, this.weibullParam);

            // Create instance of PlotGraph
            PlotGraph pg = new PlotGraph(data);
            int[] points = {4, 0};
            pg.setPoint(points);
            int[] lines = {0, 3};
            pg.setLine(lines);
            pg.setXaxisLegend("Weibull Order Statistic Medians");
            pg.setYaxisLegend("Ordered Data Values");
            pg.setGraphTitle("Weibull probability plot:   gradient = " + Fmath.truncate(this.weibullLine[1], 4) + ", intercept = "  +  Fmath.truncate(this.weibullLine[0], 4) + ",  R = " + Fmath.truncate(this.weibullCorrCoeff, 4));
            pg.setGraphTitle2("  mu = " + Fmath.truncate(this.weibullParam[0], 4) + ", sigma = "  +  Fmath.truncate(this.weibullParam[1], 4) + ", gamma = "  +  Fmath.truncate(this.weibullParam[2], 4));

            // Plot
            pg.plot();

            this.weibullDone = true;
            this.probPlotDone = true;
        }
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            // Estimates of the errors in the best fit mu, sigma and gamma
            this.probPlotStats(2, this.exponentialParam);

            // Create instance of PlotGraph
            PlotGraph pg = new PlotGraph(data);
            int[] points = {4, 0};
            pg.setPoint(points);
            int[] lines = {0, 3};
            pg.setLine(lines);
            pg.setXaxisLegend("Exponential Order Statistic Medians");
            pg.setYaxisLegend("Ordered Data Values");
            pg.setGraphTitle("Exponential probability plot:   gradient = " + Fmath.truncate(this.exponentialLine[1], 4) + ", intercept = "  +  Fmath.truncate(this.exponentialLine[0], 4) + ",  R = " + Fmath.truncate(this.exponentialCorrCoeff, 4));
            pg.setGraphTitle2("  mu = " + Fmath.truncate(this.exponentialParam[0], 4) + ", sigma = "  +  Fmath.truncate(this.exponentialParam[1], 4));

            // Plot
            pg.plot();

            this.exponentialDone = true;
            this.probPlotDone = true;
        }
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            // Estimates of the errors in the best fit mu, sigma and gamma
            this.probPlotStats(3, this.rayleighParam);

            // Create instance of PlotGraph
            PlotGraph pg = new PlotGraph(data);
            int[] points = {4, 0};
            pg.setPoint(points);
            int[] lines = {0, 3};
            pg.setLine(lines);
            pg.setXaxisLegend("Rayleigh Order Statistic Medians");
            pg.setYaxisLegend("Ordered Data Values");
            pg.setGraphTitle("Rayleigh probability plot:   gradient = " + Fmath.truncate(this.rayleighLine[1], 4) + ", intercept = "  +  Fmath.truncate(this.rayleighLine[0], 4) + ",  R = " + Fmath.truncate(this.rayleighCorrCoeff, 4));
            pg.setGraphTitle2("  beta = " + Fmath.truncate(this.rayleighParam[0], 4));

            // Plot
            pg.plot();

            this.rayleighDone = true;
            this.probPlotDone = true;
        }
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            cdata[1][i] = kpterm + sum;
            cdata[0][i] += super.deadTime;
        }

        // Plot
        PlotGraph pg = new PlotGraph(cdata);

        pg.setGraphTitle("Step Input Transient:   Step magnitude = "+stepMag);
        pg.setGraphTitle2(this.getName());
        pg.setXaxisLegend("Time");
        pg.setXaxisUnitsName("s");
        pg.setYaxisLegend("Output");
        pg.setPoint(0);
        pg.plot();
    }
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        for(int i=0; i<n; i++){
            cdata[0][i] += super.deadTime;
        }

        // Plot
        PlotGraph pg = new PlotGraph(cdata);

        pg.setGraphTitle("Ramp (a.t^n) Input Transient:   ramp gradient (a) = "+rampGradient + " ramp order (n) = " + rampOrder);
        pg.setGraphTitle2(this.getName());
        pg.setXaxisLegend("Time");
        pg.setXaxisUnitsName("s");
        pg.setYaxisLegend("Output");
        pg.setPoint(0);
        pg.plot();
    }
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            cdata[1][i] = kpterm;
        }
        if(super.deadTime!=0.0D)for(int i=0; i<n; i++)cdata[0][i] += super.deadTime;

        // Plot
        PlotGraph pg = new PlotGraph(cdata);

        pg.setGraphTitle("Step Input Transient:   Step magnitude = "+stepMag);
        pg.setGraphTitle2(this.getName());
        pg.setXaxisLegend("Time");
        pg.setXaxisUnitsName("s");
        pg.setYaxisLegend("Output");
        pg.setPoint(0);
        pg.plot();
    }
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                cdata[1][i] = rampGradient*Math.pow(cdata[0][i],rampOrder)*this.kp;
            }
            if(super.deadTime!=0.0D)for(int i=0; i<n; i++)cdata[0][i] += super.deadTime;

            // Plot
            PlotGraph pg = new PlotGraph(cdata);

            pg.setGraphTitle("Ramp (a.t^n) Input Transient:   ramp gradient (a) = "+rampGradient + " ramp order (n) = " + rampOrder);
            pg.setGraphTitle2(this.getName());
            pg.setXaxisLegend("Time");
            pg.setXaxisUnitsName("s");
            pg.setYaxisLegend("Output");
            pg.setPoint(0);
            pg.plot();
        }
    }
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            // Estimates of the errors in the best fit mu, sigma and gamma
            this.probPlotStats(4, this.paretoParam);

            // Create instance of PlotGraph
            PlotGraph pg = new PlotGraph(data);
            int[] points = {4, 0};
            pg.setPoint(points);
            int[] lines = {0, 3};
            pg.setLine(lines);
            pg.setXaxisLegend("Pareto Order Statistic Medians");
            pg.setYaxisLegend("Ordered Data Values");
            pg.setGraphTitle("Pareto probability plot:   gradient = " + Fmath.truncate(this.paretoLine[1], 4) + ", intercept = "  +  Fmath.truncate(this.paretoLine[0], 4) + ",  R = " + Fmath.truncate(this.paretoCorrCoeff, 4));
            pg.setGraphTitle2("  alpha = " + Fmath.truncate(this.paretoParam[0], 4) + ", beta = "  +  Fmath.truncate(this.paretoParam[1], 4));

            // Plot
            pg.plot();

            this.paretoDone = true;
            this.probPlotDone = true;
        }
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Related Classes of flanagan.plot.PlotGraph

  • flanagan.analysis.ProbabilityPlot
  • flanagan.analysis.Regression
  • flanagan.analysis.Stat
  • flanagan.circuits.TransmissionLine
  • flanagan.control.BlackBox
  • flanagan.control.Prop
  • flanagan.control.PropDeriv
  • flanagan.control.PropInt
  • flanagan.control.PropIntDeriv
  • flanagan.io.DigiGraph
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