我总结了绘制三元图的所有方法，这些方法超级有用！

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
## Boundary and Gridlines
fig,ax = plt.subplots()
scale = 30
figure, tax = ternary.figure(scale=scale,ax=ax)
figure.set_size_inches(6, 6)

# Draw Boundary and Gridlines
tax.boundary(linewidth=1.5)
tax.gridlines(color="black", multiple=6)
tax.gridlines(color="blue", multiple=2, linewidth=0.5)

# Set Axis labels and Title
fontsize = 12
tax.set_title("Simplex Boundary and Gridlines\n", fontsize=fontsize)
tax.left_axis_label("Left label $\\alpha^2$", fontsize=fontsize, offset=0.14)
tax.right_axis_label("Right label $\\beta^2$", fontsize=fontsize, offset=0.14)
tax.bottom_axis_label("Bottom label $\\Gamma - \\Omega$", fontsize=fontsize, offset=0.14)

# Set ticks
tax.ticks(axis='lbr', linewidth=1, multiple=5, offset=0.03)

# Remove default Matplotlib Axes
tax.clear_matplotlib_ticks()
tax.get_axes().axis('off')

ax.text(.83,-.06,'\nVisualization by DataCharm',transform = ax.transAxes,
        ha='center', va='center',fontsize = 8,color='black')
ternary.plt.show()

import math
def color_point(x, y, z, scale):
    w = 255
    x_color = x * w / float(scale)
    y_color = y * w / float(scale)
    z_color = z * w / float(scale)
    r = math.fabs(w - y_color) / w
    g = math.fabs(w - x_color) / w
    b = math.fabs(w - z_color) / w
    return (r, g, b, 1.)


def generate_heatmap_data(scale=5):
    from ternary.helpers import simplex_iterator
    d = dict()
    for (i, j, k) in simplex_iterator(scale):
        d[(i, j, k)] = color_point(i, j, k, scale)
    return d

fig,ax = plt.subplots()
scale = 80
data = generate_heatmap_data(scale)
figure, tax = ternary.figure(scale=scale,ax=ax)
figure.set_size_inches(6, 6)
tax.heatmap(data, style="hexagonal", use_rgba=True, colorbar=False)
# Remove default Matplotlib Axes
tax.clear_matplotlib_ticks()
tax.get_axes().axis('off')
tax.boundary()
tax.set_title("RGBA Heatmap")
ax.text(.83,.06,'\nVisualization by DataCharm',transform = ax.transAxes,
        ha='center', va='center',fontsize = 8,color='black')
plt.show()

test_data = data.frame(x = runif(100),
                       y = runif(100),
                       z = runif(100))
head(test_data)

library(tidyverse)
library(ggtern)
library(hrbrthemes)
library(ggtext)

test_plot_pir <- ggtern(data = test_data,aes(x, y, z))+
    geom_point(size=2.5)+
    theme_rgbw(base_family = "Roboto Condensed") +
    labs(x="",y="",
        title = "Example Density/Contour Plot: <span style='color:#D20F26'>GGtern Test</span>",
        subtitle = "processed map charts with <span style='color:#1A73E8'>ggtern()</span>",
        caption = "Visualization by <span style='color:#DD6449'>DataCharm</span>") +
    guides(color = "none", fill = "none", alpha = "none")+
    theme(
        plot.title = element_markdown(hjust = 0.5,vjust = .5,color = "black",
                             size = 20, margin = margin(t = 1, b = 12)),
        plot.subtitle = element_markdown(hjust = 0,vjust = .5,size=15),
        plot.caption = element_markdown(face = 'bold',size = 12),
        )

test_plot <- ggtern(data = test_data,aes(x, y, z),size=2)+
    stat_density_tern(geom = 'polygon',n = 300,
                      aes(fill  = ..level..,
                          alpha = ..level..))+
    geom_point(size=2.5)+
    theme_rgbw(base_family = "Roboto Condensed") +
    labs(x="",y="",
        title = "Example Density/Contour Plot: <span style='color:#D20F26'>GGtern Test</span>",
        subtitle = "processed map charts with <span style='color:#1A73E8'>ggtern()</span>",
        caption = "Visualization by <span style='color:#DD6449'>DataCharm</span>") +
    scale_fill_gradient(low = "blue",high = "red")  +
    #去除映射属性的图例
    guides(color = "none", fill = "none", alpha = "none")+ 
    theme(
        plot.title = element_markdown(hjust = 0.5,vjust = .5,color = "black",
                             size = 20, margin = margin(t = 1, b = 12)),
        plot.subtitle = element_markdown(hjust = 0,vjust = .5,size=15),
        plot.caption = element_markdown(face = 'bold',size = 12),
        )