【跟着SCI学作图】Matplotlib&Seaborn绘制散点密度图&直方图

【跟着SCI学作图】Matplotlib&Seaborn绘制散点密度图&直方图

01 引言：

最近读文献【Future increases in Arctic lightning and fire risk for permafrost carbon】发现文中图表的排版布局非常好，故借鉴一下。主要用的就是一个散点密度图+两个直方图。

论文中提供的数据如下图所示：

数据下载地址：
【https://www.nature.com/articles/s41558-021-01011-y#Sec17】

02 代码如下：

# -*- encoding: utf-8 -*-
'''
@File    :   png1.py
@Time    :   2022/12/27 20:28:31
@Author  :   HMX 
@Version :   1.0
@Contact :   [email protected]
'''
# here put the import lib
import pandas as pd
import os
import matplotlib.pyplot as plt
from scipy.stats import gaussian_kde
from mpl_toolkits.axes_grid1 import make_axes_locatable
import numpy as np
from matplotlib import ticker, cm
import seaborn as sns


os.chdir(r'E:\CODE\work\plot7\png1\data')
# part a
# scatter
df = pd.read_csv('Fig1a_XY_obs.csv',index_col=0)
x,y = df.CxP.values,df.FR.values
xy = np.vstack([x,y])
z = gaussian_kde(xy)(xy)
idx = z.argsort()
x, y, z = x[idx], y[idx], z[idx]
fig, ax = plt.subplots(figsize=(6,6),dpi=100)
xcord=x
ycord=y
m = ((xcord*ycord).mean() - xcord.mean()* ycord.mean())/(pow(xcord,2).mean()-pow(xcord.mean(),2))
c = ycord.mean() - m*xcord.mean()
scatter=ax.scatter(x,y,c=z,s = 2)
divider=make_axes_locatable(ax)
ax.set_xlim(0,0.01)
ax.set_ylim(0,1.5)
ax.set_yticks(np.arange(0,1.51,0.5))
ax_histx = divider.append_axes("bottom", 1.2, pad=0.15)
ax_histy = divider.append_axes("right", 1.2, pad=0.15)
ax.set_xticklabels([])
# line 
ds = pd.read_csv('Fig1a_XY_mod.csv',index_col=0)
print(ds)
print(ds.columns)
ys = ['Y_pl', 'Y_pl_op', 'Y_sc', 'Y_li1', 'Y_np', 'Y_mean']
linestyles = ['-.','-.','-.','-.','-.','-']
colors = ['#723BA0','#8575D7','#8DCAD0','#E0C671','#9F795D','k']
labels = ['Power law','Power law (linear opt.)','Scale','Linear','Non-parametric','Mean']
for i in range(len(ys)):
    y = ds[ys[i]].values
    ax.plot(ds.X,y,linestyle=linestyles[i],c = colors[i],label = labels[i])
    ax.legend(frameon=False)
ax.set_ylabel('Lightning flash rate (number per km$^{2}$ per month)')
ax.set_xticks([0,0.005,0.010])

# part b
df2 = pd.read_csv('Fig1b_CxP.csv',index_col=0)
sns.histplot(df2['rcp'].values, kde=True,ax = ax_histx,edgecolor = 'w',bins=51)
sns.histplot(df2['hist'].values, kde=True,ax = ax_histx,edgecolor = 'w',bins=30,color = 'red')

ax_histx.plot(1,1,label = 'Present day',c = '#D4A663')
ax_histx.plot(1,1,label = 'Future',c = '#0202FF')
ax_histx.legend(frameon=False)
ax_histx.set_ylabel('Norm. dist.')
ax_histx.set_xlabel('CAPE × Precip (W m$^{2}$)')

ax_histx.set_xticks([0,0.005,0.010])
ax_histx.set_xticklabels([0,0.005,'0.010'])
ax_histx.set_xlim(0,0.01)
ax_histx.set_ylim(0,400)
ax_histx.set_yticks([0,200,400])
# part c
df3 = pd.read_csv('Fig1c_FR.csv',index_col=0)
ax_histy.set_xlabel('Norm. dist.')
ax_histy.set_yticks([])
ax_histy = ax_histy.twinx()
ax_histy.set_ylabel('Lightning flash rate (number per km$^{2}$ per month)',rotation = 270,labelpad = 15)
sns.histplot(y = df3['rcp'].values, kde=True,ax = ax_histy,edgecolor = 'w',bins=30)
sns.histplot(y = df3['hist'].values, kde=True,ax = ax_histy,color = 'red',edgecolor = 'w',bins=30)
ax_histy.set_xlim(350,0)
ax_histy.set_xticklabels([0,2.5])
ax_histy.set_ylim(0,1.5)
ax_histy.set_yticks(np.arange(0,1.51,0.5))
ax_histy.set_xlabel('Norm. dist.')
plt.rcParams['xtick.bottom'] = plt.rcParams['xtick.labelbottom'] = True
plt.rcParams['xtick.top'] = plt.rcParams['xtick.labeltop'] = False
plt.savefig('png1.png',dpi = 600)
plt.show()

03 结果如下：

以上就是本期推文的全部内容了，如果对你有帮助的话，请‘点赞’、‘收藏’，‘关注’，你们的支持是我更新的动力。