![](data:image/jpeg;base64,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Show ticks and custom labels with annotations
Source code name: "canvas_hplot_ticks.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_ticks_6841.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")
c1.visible()
c1.setRange(0,10,0,10)
c1.setNumberOfTics(0,2)
c1.setNumberOfTics(1,5)
c1.setSubTicNumber(0,2)
c1.setSubTicNumber(1,4)
h1 = P1D("Simple1")
xpos=5
ypos=7
h1.add(xpos,ypos)
c1.draw(h1)
lab=HLabel("Point", xpos, ypos, "USER")
c1.add(lab)
c1.update()
c1.export ("example.pdf")