// Example_errBars draws points and error bars.
func Example_errBars() *plot.Plot {
type errPoints struct {
plotter.XYs
plotter.YErrors
plotter.XErrors
}
rand.Seed(int64(0))
n := 15
data := errPoints{
plotter.XYs: randomPoints(n),
plotter.YErrors: plotter.YErrors(randomError(n)),
plotter.XErrors: plotter.XErrors(randomError(n)),
}
p, err := plot.New()
if err != nil {
panic(err)
}
scatter := plotter.NewScatter(data)
scatter.Shape = plot.CrossGlyph{}
p.Add(scatter, plotter.NewXErrorBars(data), plotter.NewYErrorBars(data))
p.Add(plotter.NewGlyphBoxes())
return p
}
// Example_horizontalBoxPlots draws horizontal boxplots
// with some labels on their points.
func Example_horizontalBoxPlots() *plot.Plot {
rand.Seed(int64(0))
n := 100
uniform := make(valueLabels, n)
normal := make(valueLabels, n)
expon := make(valueLabels, n)
for i := 0; i < n; i++ {
uniform[i].Value = rand.Float64()
uniform[i].Label = fmt.Sprintf("%4.4f", uniform[i].Value)
normal[i].Value = rand.NormFloat64()
normal[i].Label = fmt.Sprintf("%4.4f", normal[i].Value)
expon[i].Value = rand.ExpFloat64()
expon[i].Label = fmt.Sprintf("%4.4f", expon[i].Value)
}
p, err := plot.New()
if err != nil {
panic(err)
}
p.Title.Text = "Horizontal Box Plot"
p.X.Label.Text = "plotter.Values"
// Make boxes for our data and add them to the plot.
uniBox := plotter.HorizBoxPlot{plotter.NewBoxPlot(vg.Points(20), 0, uniform)}
uniLabels, err := uniBox.OutsideLabels(uniform)
if err != nil {
panic(err)
}
normBox := plotter.HorizBoxPlot{plotter.NewBoxPlot(vg.Points(20), 1, normal)}
normLabels, err := normBox.OutsideLabels(normal)
if err != nil {
panic(err)
}
expBox := plotter.HorizBoxPlot{plotter.NewBoxPlot(vg.Points(20), 2, expon)}
expLabels, err := expBox.OutsideLabels(expon)
if err != nil {
panic(err)
}
p.Add(uniBox, uniLabels, normBox, normLabels, expBox, expLabels)
// Add a GlyphBox plotter for debugging.
p.Add(plotter.NewGlyphBoxes())
// Set the Y axis of the plot to nominal with
// the given names for y=0, y=1 and y=2.
p.NominalY("Uniform\nDistribution", "Normal\nDistribution",
"Exponential\nDistribution")
return p
}