作为2022年的第一篇推文，我们读者要求，介绍如何使用Python和R制作三相元图( ternary plots),涉及的知识点如下：
Python-ternary包绘制三元相图R-ggtern包绘制三元相图Python-ternary包绘制三元相图在查阅“使用Python绘制三元相图”时，我们查阅到了ternary包，该包可实现使用Python绘制三元相图的要求，官网为：https://github.com/marcharper/python-ternary，我们绘制几副官网的图例，其他样例，大家可以参考官网：
样例一：Simplex Boundary and Gridlines
代码语言：javascript代码运行次数：0运行复制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()
可视化结果如下：
样例二：RGBA colors
代码语言：javascript代码运行次数：0运行复制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()
可视化结果如下：
除了以上两个较常用的样例，官网还提供如下可视化样例(更多样例，大家可参考官网)：
Heatmaps1
Heatmaps2
Heatmaps3
R-ggtern包绘制三元相图在介绍了Python 绘制三元相图之后，我们再介绍使用R绘制，由于ggplot2的强大功能，我们还是选择ggplot2体系的第三方包进行绘制，而ggtern包则是我们的首要选择。官网：http://www.ggtern.com/。我们虚构数据进行ggtern包的基本探索，具体如下：
数据构建如下：
代码语言：javascript代码运行次数：0运行复制test_data = data.frame(x = runif(100),
y = runif(100),
z = runif(100))
head(test_data)
预览如下：
point charts：
代码语言：javascript代码运行次数：0运行复制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: GGtern Test",
subtitle = "processed map charts with ggtern()",
caption = "Visualization by DataCharm") +
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),
)
可视化结果如下：
优化处理：
代码语言：javascript代码运行次数：0运行复制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: GGtern Test",
subtitle = "processed map charts with ggtern()",
caption = "Visualization by DataCharm") +
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),
)
可视化结果如下：
除此之外，官网还提供如下样例：
PPS 3-State Model
using geom_label_viewport
Ternary Tribin
Demonstration of Raster Annotation
当然，还有一个交互式的demo可以更好的体验ggtern包的强大，界面如下：
总结本期推文我们汇总了Python和R绘制了三元相图，整体难度较低，小伙伴们可行自己参考官网进行探索。接下来，我们还会进行优质数据的免费分享哦！