Python绘制散点密度图的三种方式详解

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)
# 读取数据
import pandas as pd
filename=r\'F:/Rpython/lp37/testdata.xlsx\'
df2=pd.read_excel(filename)#读取文件
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(\"估计值\",fontdict=font3)
plt.xlabel(\"预测值\",fontdict=font3)
plt.savefig(\'F:/Rpython/lp37/plot70.png\',dpi=800,bbox_inches=\'tight\',pad_inches=0)
plt.show()

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为两个需要做对比分析的两个量。
    # ==========计算评价指标==========
    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(\'==========算法评价指标==========\')
    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)  # 画的1:1线，线的颜色为black，线宽为0.8
    plt.plot(x,regression_line,\'red\',lw=1.5)      # 预测与实测数据之间的回归线
    plt.axis([0,25,0,25])  # 设置线的范围
    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的位置需要根据x,y的大小范围进行调整。
    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)                                  # 设置x坐标轴的显示范围
    plt.ylim(0,25)                                  # 设置y坐标轴的显示范围
    plt.savefig(\'F:/Rpython/lp37/plot71.png\',dpi=800,bbox_inches=\'tight\',pad_inches=0)
    plt.show()

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)
# 读取数据
filename=r\'F:/Rpython/lp37/testdata.xlsx\'
df2=pd.read_excel(filename)#读取文件
x=df2[\'data1\'].values.ravel()
y=df2[\'data2\'].values.ravel()
N = len(df2[\'data1\'])
#绘制拟合线
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))
#开始绘图
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()