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散點密度圖是在散點圖的基礎上，計算了每個散點周圍分布了多少其他的點，并通過顏色表現出來。本文主要介紹了Python繪制散點密度圖的三種方式，需要的可以參考下

方式一

import matplotlib.pyplot as plt
import numpy as np
from scipy.stats import gaussian_kde
from mpl_toolkits.axes_grid1 import make_axes_locatable
from matplotlib import rcParams
config = {"font.family":'Times New Roman',"font.size": 16,"mathtext.fontset":'stix'}
rcParams.update(config)
# 讀取數據
import pandas as pd
filename=r'F:/Rpython/lp37/testdata.xlsx'
df2=pd.read_excel(filename)#讀取文件
x=df2['data1'].values
y=df2['data2'].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=(12,9),dpi=100)
scatter=ax.scatter(x,y,marker='o',c=z,edgecolors='',s=15,label='LST',cmap='Spectral_r')
cbar=plt.colorbar(scatter,shrink=1,orientation='vertical',extend='both',pad=0.015,aspect=30,label='frequency') #orientation='horizontal'
font3={'family':'SimHei','size':16,'color':'k'}
plt.ylabel("估計值",fontdict=font3)
plt.xlabel("預測值",fontdict=font3)
plt.savefig('F:/Rpython/lp37/plot70.png',dpi=800,bbox_inches='tight',pad_inches=0)
plt.show()

方式二

from statistics import mean
import matplotlib.pyplot as plt
from sklearn.metrics import explained_variance_score,r2_score,median_absolute_error,mean_squared_error,mean_absolute_error
from scipy import stats
import numpy as np
from matplotlib import rcParams
config = {"font.family":'Times New Roman',"font.size": 16,"mathtext.fontset":'stix'}
rcParams.update(config)
def scatter_out_1(x,y): ## x,y為兩個需要做對比分析的兩個量。
????# ==========計算評價指標==========
????BIAS = mean(x - y)
????MSE = mean_squared_error(x, y)
????RMSE = np.power(MSE, 0.5)
????R2 = r2_score(x, y)
????MAE = mean_absolute_error(x, y)
????EV = explained_variance_score(x, y)
????print('==========算法評價指標==========')
????print('BIAS:', '%.3f' % (BIAS))
????print('Explained Variance(EV):', '%.3f' % (EV))
????print('Mean Absolute Error(MAE):', '%.3f' % (MAE))
????print('Mean squared error(MSE):', '%.3f' % (MSE))
????print('Root Mean Squard Error(RMSE):', '%.3f' % (RMSE))
????print('R_squared:', '%.3f' % (R2))
????# ===========Calculate the point density==========
????xy = np.vstack([x, y])
????z = stats.gaussian_kde(xy)(xy)
????# ===========Sort the points by density, so that the densest points are plotted last===========
????idx = z.argsort()
????x, y, z = x[idx], y[idx], z[idx]
????def best_fit_slope_and_intercept(xs, ys):
????????m = (((mean(xs) * mean(ys)) - mean(xs * ys)) / ((mean(xs) * mean(xs)) - mean(xs * xs)))
????????b = mean(ys) - m * mean(xs)
????????return m, b
????m, b = best_fit_slope_and_intercept(x, y)
????regression_line = []
????for a in x:
????????regression_line.append((m * a) + b)
????fig,ax=plt.subplots(figsize=(12,9),dpi=600)
????scatter=ax.scatter(x,y,marker='o',c=z*100,edgecolors='',s=15,label='LST',cmap='Spectral_r')
????cbar=plt.colorbar(scatter,shrink=1,orientation='vertical',extend='both',pad=0.015,aspect=30,label='frequency')
????plt.plot([0,25],[0,25],'black',lw=1.5)? # 畫的1:1線，線的顏色為black，線寬為0.8
????plt.plot(x,regression_line,'red',lw=1.5)????? # 預測與實測數據之間的回歸線
????plt.axis([0,25,0,25])? # 設置線的范圍
????plt.xlabel('OBS',family = 'Times New Roman')
????plt.ylabel('PRE',family = 'Times New Roman')
????plt.xticks(fontproperties='Times New Roman')
????plt.yticks(fontproperties='Times New Roman')
????plt.text(1,24, '$N=%.f$' % len(y), family = 'Times New Roman') # text的位置需要根據x,y的大小范圍進行調整。
????plt.text(1,23, '$R^2=%.3f$' % R2, family = 'Times New Roman')
????plt.text(1,22, '$BIAS=%.4f$' % BIAS, family = 'Times New Roman')
????plt.text(1,21, '$RMSE=%.3f$' % RMSE, family = 'Times New Roman')
????plt.xlim(0,25)????????????????????????????????? # 設置x坐標軸的顯示范圍
????plt.ylim(0,25)????????????????????????????????? # 設置y坐標軸的顯示范圍
????plt.savefig('F:/Rpython/lp37/plot71.png',dpi=800,bbox_inches='tight',pad_inches=0)
????plt.show()

方式三

import pandas as pd
import numpy as np
from scipy import optimize
import matplotlib.pyplot as plt
from matplotlib import cm
from matplotlib.colors import Normalize
from scipy.stats import gaussian_kde
from matplotlib import rcParams
config={"font.family":'Times New Roman',"font.size":16,"mathtext.fontset":'stix'}
rcParams.update(config)
# 讀取數據
filename=r'F:/Rpython/lp37/testdata.xlsx'
df2=pd.read_excel(filename)#讀取文件
x=df2['data1'].values.ravel()
y=df2['data2'].values.ravel()
N = len(df2['data1'])
#繪制擬合線
x2 = np.linspace(-10,30)
y2 = x2
def f_1(x,A,B):
????return A*x + B
A1,B1 = optimize.curve_fit(f_1,x,y)[0]
y3 = A1*x + B1
# Calculate the point density
xy = np.vstack([x,y])
z = gaussian_kde(xy)(xy)
norm = Normalize(vmin = np.min(z), vmax = np.max(z))
#開始繪圖
fig,ax=plt.subplots(figsize=(12,9),dpi=600)
scatter=ax.scatter(x,y,marker='o',c=z*100,edgecolors='',s=15,label='LST',cmap='Spectral_r')
cbar=plt.colorbar(scatter,shrink=1,orientation='vertical',extend='both',pad=0.015,aspect=30,label='frequency')
cbar.ax.locator_params(nbins=8)
cbar.ax.set_yticklabels([0.005,0.010,0.015,0.020,0.025,0.030,0.035])#0,0.005,0.010,0.015,0.020,0.025,0.030,0.035
ax.plot(x2,y2,color='k',linewidth=1.5,linestyle='--')
ax.plot(x,y3,color='r',linewidth=2,linestyle='-')
fontdict1 = {"size":16,"color":"k",'family':'Times New Roman'}
ax.set_xlabel("PRE",fontdict=fontdict1)
ax.set_ylabel("OBS",fontdict=fontdict1)
# ax.grid(True)
ax.set_xlim((0,25))
ax.set_ylim((0,25))
ax.set_xticks(np.arange(0,25.1,step=5))
ax.set_yticks(np.arange(0,25.1,step=5))
plt.savefig('F:/Rpython/lp37/plot72.png',dpi=800,bbox_inches='tight',pad_inches=0)
plt.show()