![](data:image/jpeg;base64,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) 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Data in X-Y, plotting on 2D canvas
Source code name: "data_p1d_1.py"
Programming language: Python
Topic: Data arrays/P1D
DMelt Version 1. Last modified: 12/08/2015. License: Free
https://datamelt.org/code/cache/data_p1d_1_4007.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 *
from java.awt import Color
p1=P1D("1st data set")
p1.add(1,2)
p1.add(2,3)
p1.add(4,5)
p2=P1D("2nd data set")
p2.add(-1,3)
p2.add(5,-2)
p2.add(1,0)
p2.setColor(Color.red)
c1 = HPlot("Canvas")
c1.visible()
c1.setAutoRange()
c1.draw(p1)
c1.draw(p2)