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) 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Shwoing X-Y data on 4 canvas regions
Source code name: "canvas_hplot.py"
Programming language: Python
Topic: Plots/2D
DMelt Version 1.4. Last modified: 12/14/1970. License: Free
https://datamelt.org/code/cache/canvas_hplot_5602.py
To run this script using the DMelt IDE,
copy the above URL link to the menu [File]→[Read script from URL] of the DMelt IDE.
from jhplot import *
c1 = HPlot("Canvas",500,400,2,2)
c1.visible()
c1. setRangeAll(0,10,0,10)
h1 = P1D("Simple")
c1.cd(1,1)
h1.add(5,6); c1.draw(h1)
c1.cd(1,2)
h1.add(4,3); c1.draw(h1)
c1.cd(2,1)
h1.add(3,3); c1.draw(h1)
c1.cd(2,2)
h1.add(2,1); c1.draw(h1)
c1.export ("example.pdf") # export to PDF