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Python (Matplotlib) - Tick marks on ternary plot

I am trying to draw tines that look like tick-marks on the sides of an equilateral triangle. I am doing this using Matplotlib in Python.

Here is the equilateral triangle***:

import matplotlib.pyplot as plt
from pylab import *
fig = figure()
ax = fig.add_subplot(111)
ax.axis('equal')
ax.plot([1, 0.5], [0, 0.866], 'k-')
ax.plot([0, 0.5], [0, 0.866], 'k-')
ax.plot([0, 1], [0, 0], 'k-')
ax.plot([0.5, 1.01, 0.1], [-0.01, 0.5, 0.88], 'w.') #base boundary
plt.show()

The resulting image is seen here.triangle_from_code.

Now, I would like to add lines that look like tick marks. The lines would be drawn like seen here from 1:07-1:29 and from 2:19-2:32.

I would like the output to look something like this image.enter image description here

Is there a way to draw these lines with Matplotlib?

EDIT: *** triangle code as per here

like image 666
edesz Avatar asked Jun 21 '15 18:06

edesz


1 Answers

This does more or less what you want:

from __future__ import division
import matplotlib.pyplot as plt
import numpy as np

def plot_ticks(start, stop, tick, n):
    r = np.linspace(0, 1, n+1)
    x = start[0] * (1 - r) + stop[0] * r
    x = np.vstack((x, x + tick[0]))
    y = start[1] * (1 - r) + stop[1] * r
    y = np.vstack((y, y + tick[1]))
    plt.plot(x, y, 'k', lw=1)

n = 10
tick_size = 0.2
margin = 0.05

# define corners of triangle    
left = np.r_[0, 0]
right = np.r_[1, 0]
top = np.r_[0.5, 3**0.5 / 2]
triangle = np.c_[left, right, top, left]

# define vectors for ticks
bottom_tick = tick_size * (right - top) / n
right_tick = tick_size * (top - left) / n
left_tick = tick_size * (left - right) / n

plt.plot(triangle[0], triangle[1], 'k', lw=2) 
plot_ticks(left, right, bottom_tick, n)
plot_ticks(right, top, right_tick, n)
plot_ticks(left, top, left_tick, n)
plt.axis([left[0]-margin, right[0]+margin, left[1]-margin, top[1]+margin])
plt.gca().set_aspect('equal', adjustable='box')
plt.show()    

Result: enter image description here

like image 142
Bas Swinckels Avatar answered Nov 15 '22 09:11

Bas Swinckels