from jhplot import HChart,P1D
c1 = HChart("Canvas",600,500, 2, 1)
c1.setGTitle("Polar coordinates")
c1.visible()
c1.cd(1,1)
c1.setName("Polar coordinates-I")
c1.setChartPolar()
p1= P1D("test 1")
p2= P1D("test 2")
# fill
rand = Random()
for i in range(20):
x=4.0*i # x-value
p1.add(i*4, 10.0*rand.nextGaussian());
p2.add(i*2, 5.0*rand.nextGaussian());
c1.add(p1)
c1.add(p2)
c1.update()
c1.cd(2,1)
p3= P1D("Example")
for i in range(0,3*360,5):
p3.add( 90-i,i)
c1.setChartPolar()
c1.setName("Polar coordinates-II")
c1.add(p3)
c1.update()
# export to some image (png,eps,pdf,jpeg...)
# NeuralNetwork. Build a bayesian Self-Organizing Map. Example II
from jhplot import *
from java.awt import Color
from java.util import Random
c1 = HPlot("Canvas")
c1.setGTitle("Bayesian Self-Organizing Map")
c1.visible()
c1.setAutoRange()
h1 = H1D("Data",20, -100.0, 300.0)
r = Random()
for i in range(2000):
h1.fill(100+r.nextGaussian()*100)
p1d=P1D(h1,0,0)
p1d.setErrToZero(1)
bs=HBsom()
bs.setNPoints(30)
bs.setData(p1d)
bs.run()
result=bs.getResult()
result.setStyle("pl")
result.setColor(Color.blue)
c1.draw(p1d)
c1.draw(result)
#@OUTPUT Integer series1_strokeWidth
series1_strokeColor = "rgb(" + str(r.nextInt(255)) + "," + str(
r.nextInt(255)) + "," + str(r.nextInt(255)) + ")"
series1_fillColor = series1_strokeColor
series1_strokeWidth = 1
series1_xvalues = []
series1_yvalues = []
series1_error = []
currentY = 0
for i in range(29):
series1_xvalues.append(i)
series1_yvalues.append(currentY)
currentY += r.nextGaussian()
series1_error.append(abs(r.nextGaussian()))
r = Random()
# Series 2 Outputs
#@OUTPUT Double[] series2_xvalues
#@OUTPUT Double[] series2_yvalues
#@OUTPUT Double[] series2_error
#@OUTPUT String series2_fillColor
#@OUTPUT String series2_strokeColor
#@OUTPUT Integer series2_strokeWidth
series2_strokeColor = "rgb(" + str(r.nextInt(255)) + "," + str(
r.nextInt(255)) + "," + str(r.nextInt(255)) + ")"
series2_fillColor = series1_strokeColor
from java.awt import Color
c1 = HPlot3D("Canvas", 500, 500)
c1.setGTitle("Interactive 3D galaxy")
c1.setRange(-10, 10, -10, 10, -10, 10)
c1.setNameX("X")
c1.setNameY("Y")
c1.visible(1)
# create P2D objects in 3D
p1 = P2D("Galaxy")
p1.setSymbolSize(2)
p1.setSymbolColor(Color.blue)
rand = Random()
for i in range(5000):
x = 3 * rand.nextGaussian()
y = 3 * rand.nextGaussian()
z = 0.4 * rand.nextGaussian()
p1.add(x, y, z)
c1.draw(p1)
h2 = P2D("Core")
h2.setSymbolSize(2)
h2.setSymbolColor(Color.yellow)
for i in range(5000):
x = 0.9 * rand.nextGaussian()
y = 0.9 * rand.nextGaussian()
z = 0.8 * rand.nextGaussian()
h2.add(x, y, z)
c1.draw(h2)
# Data clustering | C | 1.7 | S.Chekanov | Perform a cluster analysis using jMinHEP GUI
from java.util import Random
from jminhep.cluster import *
from jhplot import *
# create data for analysis
data = DataHolder("Example")
# fill 3D data with Gaussian random numbers
rand = Random()
for i in range(100):
a =[]
a.append( 10*rand.nextGaussian() )
a.append( 2*rand.nextGaussian()+1 )
a.append( 10*rand.nextGaussian()+3 )
data.add( DataPoint(a) )
# start jMinHEP GUI
c1=HCluster(data)
c1 = HPlot3D("Canvas",500,500)
c1.setGTitle("Interactive 3D galaxy")
c1.setRange(-10,10,-10,10,-10,10)
c1.setNameX("X")
c1.setNameY("Y")
c1.visible(1)
# create P2D objects in 3D
p1= P2D("Galaxy")
p1.setSymbolSize(2);
p1.setSymbolColor(Color.blue);
rand = Random()
for i in range(5000):
x=3*rand.nextGaussian()
y=3*rand.nextGaussian()
z=0.4*rand.nextGaussian()
p1.add(x,y,z)
c1.draw(p1)
h2=P2D("Core")
h2.setSymbolSize(2)
h2.setSymbolColor(Color.yellow)
for i in range(5000):
x=0.9*rand.nextGaussian()
y=0.9*rand.nextGaussian()
z=0.8*rand.nextGaussian()
h2.add(x,y,z)
c1.draw(h2)
c1.valueLine(8.0, "First", "category3");
c1.valueLine(3.0, "Second", "category1");
c1.valueLine(2.8, "Second", "category2");
c1.valueLine(4.0, "Second", "category3");
c1.valueLine(1.0, "Second", "category3");
c1.update()
# new plot. Fill a histogram
h1=H1D("histogram",20,-2,2)
h1.setFillColor(Color.blue)
rand = Random()
for i in range(1000):
h1.fill(rand.nextGaussian())
c1.cd(2,1)
c1.setName("Histogram example")
c1.setRange(0,-2,2)
c1.setRange(1,0,100)
c1.add(h1)
c1.update()
c1.export("a.pdf")
# export to some image (png,eps,pdf,jpeg...)
# c1.export(Editor.DocMasterName()+".png")
import math
from java.util import Random
from java.lang import Double
r = Random()
# Series 1 Outputs
#@OUTPUT Double[] series1_xvalues
#@OUTPUT Double[] series1_yvalues
#@OUTPUT Double[] series1_size
#@OUTPUT String[] series1_label
#@OUTPUT String[] series1_color
#@OUTPUT String series1_markerColor
series1_markerColor = "lightgreen"
series1_xvalues = []
series1_yvalues = []
series1_size = []
series1_color = []
series1_label = []
for i in range(99):
series1_xvalues.append(Double.valueOf(r.nextGaussian()))
series1_yvalues.append(Double.valueOf(r.nextGaussian()))
series1_size.append(
Double.valueOf(4 / math.sqrt(series1_xvalues[i] * series1_xvalues[i] +
series1_yvalues[i] * series1_yvalues[i])))
series1_color.append("rgb(" + str(r.nextInt(255)) + "," +
str(r.nextInt(255)) + "," + str(r.nextInt(255)) + ")")
series1_label.append("point " + str(i))
# NeuralNetwork. Build a bayesian Self-Organizing Map. Example I
from java.util import Random
from jhplot import *
h1 = H1D("Data",20, -100.0, 300.0)
r = Random()
for i in range(2000):
h1.fill(100+r.nextGaussian()*100)
h1.fill(100+r.nextDouble()*100)
p1d=P1D(h1,0,0)
# write to a file
p1d.toFile("data.txt")
bs=HBsom()
bs.setNPoints(30)
bs.setData(p1d)
bs.visible()
r = Random()
# Series 1 Outputs
#@OUTPUT Double[] series1_values
#@OUTPUT String series1_strokeColor
#@OUTPUT Integer series1_strokeWidth
series1_strokeColor = "rgb(" + str(r.nextInt(255)) + "," + str(
r.nextInt(255)) + "," + str(r.nextInt(255)) + ")"
series1_strokeWidth = 2
series1_values = []
for i in range(999):
series1_values.append(Double.valueOf(r.nextGaussian()))
# Series 2 Outputs
#@OUTPUT Double[] series2_values
#@OUTPUT String series2_strokeColor
#@OUTPUT Integer series2_strokeWidth
series2_strokeColor = "rgb(" + str(r.nextInt(255)) + "," + str(
r.nextInt(255)) + "," + str(r.nextInt(255)) + ")"
series2_strokeWidth = 2
series2_values = []
for i in range(999):
series2_values.append(Double.valueOf(r.nextGaussian() - 5))
c1.cd(1, 1)
c1.setChartPie()
c1.setName("Pie example")
c1.valuePie("Hamburg", 1.0)
c1.valuePie("London", 2.0)
c1.valuePie("Paris", 1.0)
c1.valuePie("Bern", 1.0)
c1.update()
# new plot
c1.cd(1, 2)
c1.setName("XY example")
c1.setNameX("weeks")
c1.setNameY("density")
p1 = P1D("test 1")
p1.setColor(Color.red)
p2 = P1D("test 2")
# fill
rand = Random()
for i in range(1000):
x = 4.0 * i # x-value
p1.add(i * 4, 10.0 * rand.nextGaussian())
p2.add(i * 2, 5.0 * rand.nextGaussian())
c1.add(p1)
c1.add(p2)
c1.update()
# export to some image (png,eps,pdf,jpeg...)
# c1.export(Editor.DocMasterName()+".png")
# 3D Plots | C | 1.7 | S.Chekanov | 3D histogram (H2D) and a 3D (F2D) function overlyed on HPlot3D
from jhplot import HPlot3D, H2D, F2D
from java.util import Random
c1 = HPlot3D("Canvas", 600, 400)
c1.setGTitle("F2D and H2D objects")
c1.setTextBottom("Global X")
c1.setTextLeft("Global Y")
c1.setNameX("X")
c1.setNameY("Y")
c1.setColorMode(4)
c1.visible(1)
h1 = H2D("My 2D Test 1", 30, -3.0, 3.0, 30, -3.0, 3.0)
f1 = F2D("8*(x*x+y*y)", -3.0, 3.0, -3.0, 5.0)
rand = Random()
for i in range(1000):
h1.fill(0.4 * rand.nextGaussian(), rand.nextGaussian())
c1.draw(h1, f1)
# export to some image (png,eps,pdf,jpeg...)
# c1.export(Editor.DocMasterName()+".svg")
c1.export("image.pdf")
c1.export("image.eps")
