工具包 | 使用Python绘制Sci学术期刊配图

气象学家

发布于 2020-10-09 14:59:13

9.6K0

发布于 2020-10-09 14:59:13

之前还在讨论Matplotlib没有很好的第三方主题库呢？这不，又被我发现了一个宝藏库，还专门用于一些学术期刊的图表发表，可谓是弥补了matplotlib 繁琐的自定义设置。好了，话不多说，今天这篇推文的主角就是Github 上拥有1.6k 星之多的科学论文图表绘制库「SciencePlots」。推文的主要内如下：

SciencePlots 库介绍
SciencePlots 实例演示

SciencePlots 库介绍

使用Python-matplotlib绘制科研图表，其默认的颜色和格式并不能满足一般的期刊要求，若想要符合要求，就必须自定义设置，而这个步骤在对面对多幅图表时就显得繁琐和重复。在之前的几篇科学图表绘制推文中Python-matplotlib 学术散点图 EE 统计及绘制 Python-matplotlib 横向堆积柱状图绘制就定制化表格编写了较多定制化代码。而SciencePlots就是为解决科研图表繁琐设置而定制的一系列科研绘图样式库，可以绘制很合适科研发表的图表。

安装

我们直接可使用pip 直接进行安装，但想要安装最新版本可以采用如下方式：

pip install git+https://github.com/garrettj403/SciencePlots.git

如使用pip稳定安装，则直接使用如下代码：

# for lastest release
pip install SciencePlots

主要样式

SciencePlots 提供了多种用于不同期刊发表要求的主题，接下来我们主要介绍几种主题，注意：由于SciencePlots默认使用Latex字体渲染，而相应软件安装较为麻烦，以下样例均采用禁用Latex字体渲染，即设置 no-latex (数据还是使用官网提供的)：

数据构建:

import numpy as np 
import matplotlib.pyplot as plt 
def model(x, p):
    return x ** (2 * p + 1) / (1 + x ** (2 * p))
x = np.linspace(0.75, 1.25, 201)

「原始主题」

fig, ax = plt.subplots(figsize=(4,3),dpi=200)
for p in [10, 15, 20, 30, 50, 100]:
    ax.plot(x, model(x, p), label=p)
ax.legend(title='Order')
ax.set(xlabel='Voltage (mV)')
ax.set(ylabel='Current ($\mu$A)')
ax.autoscale(tight=True)
fig.savefig(r'F:\DataCharm\学术图表绘制\Python-matplotlib\SciencePlots\fig1.png', 
            width=4,height=3,dpi=600)

结果如下：

「science」

with plt.style.context(['science','no-latex']):
    fig, ax = plt.subplots(figsize=(4,3),dpi=200)
    for p in [10, 15, 20, 30, 50, 100]:
        ax.plot(x, model(x, p), label=p)
    ax.legend(title='Order')
    ax.set(xlabel='Voltage (mV)')
    ax.set(ylabel='Current ($\mu$A)')
    ax.autoscale(tight=True)
    fig.savefig(r'F:\DataCharm\学术图表绘制\Python-matplotlib\SciencePlots\fig1_science.png', 
                width=4,height=3,dpi=600)

结果如下：

可以发现：刻度、颜色和图例等发生明显改变。

「IEEE」

with plt.style.context(['ieee','no-latex']):
    fig, ax = plt.subplots(figsize=(4,3),dpi=200)
    for p in [10, 20, 50]:
        ax.plot(x, model(x, p), label=p)
    ax.legend(title='Order')
    ax.set(xlabel='Voltage (mV)')
    ax.set(ylabel='Current ($\mu$A)')
    ax.autoscale(tight=True)
    fig.savefig(r'F:\DataCharm\学术图表绘制\Python-matplotlib\SciencePlots\fig1_ieee.png', 
                width=4,height=3,dpi=600)

结果如下：

当然你还可以进行主题组合：「dark_background+science+high-vis」

with plt.style.context(['dark_background', 'science', 'high-vis','no-latex']):
    fig, ax = plt.subplots(figsize=(4,3),dpi=200)
    for p in [10, 15, 20, 30, 50, 100]:
        ax.plot(x, model(x, p), label=p)
    ax.legend(title='Order')
    ax.set(xlabel='Voltage (mV)')
    ax.set(ylabel='Current ($\mu$A)')
    ax.autoscale(tight=True)
    fig.savefig(r'F:\DataCharm\学术图表绘制\Python-matplotlib\SciencePlots\fig1_science_dark_background.png', 
                width=4,height=3,dpi=600)

结果如下：

更多的例子，大家可以直接去官网进行测试，接下来，我们将主题应用到我们之前绘制过的科学图表绘图代码上。

SciencePlots实例应用

多类别散点图

science：

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
plt.style.use(['science', 'no-latex'])
#plt.rcParams['font.family'] = "Times New Roman"
x = np.arange(0,len(scatter),1)
y1 = scatter.D1.values
y2 = scatter.D2.values
y3 = scatter.D3.values
y4 = scatter.D4.values
y5 = scatter.D5.values
y6 = scatter.D6.values

fig,ax = plt.subplots(figsize=(4,3),dpi=300)
scatter_01 = ax.plot(x,y1,marker='s',lw=.5,label='D1')
scatter_02 = ax.plot(x,y2,marker='s',ls='--',lw=.5,label='D2')
scatter_03 = ax.plot(x,y3,marker='o',lw=.8,ls=':',label='D3')
scatter_04 = ax.plot(x,y4,marker='o',lw=.5,label='D4')

scatter_05 = ax.plot(x,y5,marker='^',lw=.5,ls='-.',label='D5')
scatter_06 = ax.plot(x,y6,marker='^',ls='--',lw=.5,label='D6')
ax.set_ylim(bottom=10,top=45)

ax.legend(frameon=False,ncol=3,loc='upper center',fontsize=8.5)
text_font = {'size':'15','weight':'bold','color':'black'}
ax.text(.88,.88,"(a)",transform = ax.transAxes,fontdict=text_font,zorder=4)
ax.text(.87,-.08,'\nVisualization by DataCharm',transform = ax.transAxes,
        ha='center', va='center',fontsize = 4,color='black',fontweight='bold',family='Roboto Mono')
plt.savefig(r'F:\DataCharm\学术图表绘制\Python-matplotlib\SciencePlots\class_scatter_science.png',width=5,height=3,
            dpi=900,bbox_inches='tight')
plt.show()

结果如下：

ieee：

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
plt.style.use(['ieee', 'no-latex'])
#plt.rcParams['font.family'] = "Times New Roman"
x = np.arange(0,len(scatter),1)
y1 = scatter.D1.values
y2 = scatter.D2.values
y3 = scatter.D3.values
y4 = scatter.D4.values
y5 = scatter.D5.values
y6 = scatter.D6.values

fig,ax = plt.subplots(figsize=(4,3),dpi=300)
scatter_01 = ax.plot(x,y1,marker='s',lw=.5,label='D1')
scatter_02 = ax.plot(x,y2,marker='s',ls='--',lw=.5,label='D2')
scatter_03 = ax.plot(x,y3,marker='o',lw=.8,ls=':',label='D3')
scatter_04 = ax.plot(x,y4,marker='o',lw=.5,label='D4')

scatter_05 = ax.plot(x,y5,marker='^',lw=.5,ls='-.',label='D5')
scatter_06 = ax.plot(x,y6,marker='^',ls='--',lw=.5,label='D6')
ax.set_ylim(bottom=10,top=45)

ax.legend(frameon=False,ncol=3,loc='upper center',fontsize=8.5)
text_font = {'size':'15','weight':'bold','color':'black'}
ax.text(.88,.88,"(a)",transform = ax.transAxes,fontdict=text_font,zorder=4)
ax.text(.87,-.08,'\nVisualization by DataCharm',transform = ax.transAxes,
        ha='center', va='center',fontsize = 4,color='black',fontweight='bold',family='Roboto Mono')
plt.savefig(r'F:\DataCharm\学术图表绘制\Python-matplotlib\SciencePlots\class_scatter_ieee.png',width=5,height=3,
            dpi=900,bbox_inches='tight')
plt.show()

结果如下：

多类别柱形图

science + high-contrast

plt.style.use(['science', 'high-contrast','no-latex'])

labels = ['L1', 'L2', 'L3', 'L4', 'L5']
data_a = [20, 34, 30, 35, 27]
data_b = [25, 32, 34, 20, 25]
data_c = [12, 20, 24, 17, 16]
x = np.arange(len(labels))
width = .25
#plots
fig,ax = plt.subplots(figsize=(5,3),dpi=200)
bar_a = ax.bar(x-width/2, data_a,width,label='category_A')
bar_b = ax.bar(x+width/2, data_b, width,label='category_B')
bar_c = ax.bar(x+width*3/2, data_c,width,label='category_C')
ax.set_xticks(x+.1)
ax.set_xticklabels(labels,size=10)
ax.legend()    

text_font = {'size':'14','weight':'bold','color':'black'}
ax.text(.03,.9,"(a)",transform = ax.transAxes,fontdict=text_font,zorder=4)
ax.text(.87,-.08,'\nVisualization by DataCharm',transform = ax.transAxes,
        ha='center', va='center',fontsize = 5,color='black',fontweight='bold',family='Roboto Mono')
plt.savefig(r'F:\DataCharm\学术图表绘制\Python-matplotlib\SciencePlots\bar_sci_high-contrast.png',width=5,height=3,
            dpi=900,bbox_inches='tight')
plt.show()

结果如下：