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散点密度图是在散点图的基础上,计算了每个散点周围分布了多少其他的点,并通过颜色表现出来。本文主要介绍了Python绘制散点密度图的三种方式,需要的可以参考下
方式一
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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' 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] 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() |
方式二
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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) 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.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() |
方式三
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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.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() |
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