Python-matplotlib 学术散点图 EE 统计及绘制

#导入数据拟合函数from scipy.stats import linregress  x2 = np.linspace(-10,10)#制作最佳拟合线数据y2=x2#制作上拟合线数据up_y2 = 1.15*x2 + 0.05#制作下拟合线数据down_y2 = 0.85*x2 - 0.05#进行拟合line_01 = linregress(x2,y2)line_top = linregress(x2,up_y2)line_bopptom = linregress(x2,down_y2)

data_select = data_select.copy()
data_select['true_y'] = data_select.true_data.valuesdata_select['top_y'] = data_select['true_data'].apply(lambda x : line_top[0]*x + line_top[1])data_select['bottom_y'] = data_select['true_data'].apply(lambda x : line_bopptom[0]*x + line_bopptom[1])data_select.head()

#构建 选择条件top_condi = (data_select['model01_estimated'] > data_select['top_y'])bottom_condi = (data_select['model01_estimated'] < data_select['bottom_y'])bottom_top = ((data_select['model01_estimated'] < data_select['top_y']) &               (data_select['model01_estimated'] > data_select['bottom_y']))all_data = len(data_select)top_counts = len(data_select[top_condi])bottom_counts = len(data_select[bottom_condi])bottom_top_counts = len(data_select[bottom_top])

all_data = 4348top_counts = 1681bottom_counts = 404bottom_top_counts = 2263

label_font = {'size':'22','weight':'medium','color':'black'}ax.text(.7,.25,s='Within EE = ' + '{:.0%}'.format(bottom_top_counts/all_data),transform = ax.transAxes,        ha='left', va='center',fontdict=text_font)ax.text(.7,.18,s='Above EE = ' + '{:.0%}'.format(top_counts/all_data),transform = ax.transAxes,        ha='left', va='center',fontdict=text_font)ax.text(.7,.11,s='Below EE = ' + '{:.0%}'.format(bottom_counts/all_data),transform = ax.transAxes,        ha='left', va='center',fontdict=text_font)

import pandas as pdimport numpy as npfrom scipy import optimizeimport matplotlib.pyplot as pltfrom sklearn.metrics import mean_squared_error,r2_scorefrom matplotlib.pyplot import MultipleLocator#统一修改字体plt.rcParams['font.family'] = ['Arial']
N = len(test_data['true_data'])x = test_data['true_data'].values.ravel() #真实值y = test_data['model01_estimated'].values.ravel()#预测值C=round(r2_score(x,y),4)rmse = round(np.sqrt(mean_squared_error(x,y)),3)#绘制拟合线x2 = np.linspace(-10,10)y2=x2def f_1(x, A, B):      return A*x + B  A1, B1 = optimize.curve_fit(f_1, x, y)[0]  y3 = A1*x + B1#开始绘图fig, ax = plt.subplots(figsize=(7,5),dpi=200)ax.scatter(x, y,edgecolor=None, c='k', s=12,marker='s')ax.plot(x2,y2,color='k',linewidth=1.5,linestyle='-',zorder=2)ax.plot(x,y3,color='r',linewidth=2,linestyle='-',zorder=2)
#添加上线和下线ax.plot(x2,up_y2,color='k',lw=1.,ls='--',zorder=2,alpha=.8)ax.plot(x2,down_y2,color='k',lw=1.,ls='--',zorder=2,alpha=.8)fontdict1 = {"size":17,"color":"k",}ax.set_xlabel("True Values", fontdict=fontdict1)ax.set_ylabel("Estimated Values ", fontdict=fontdict1)ax.grid(which='major',axis='y',ls='--',c='k',alpha=.7)ax.set_axisbelow(True)ax.set_xlim((0, 2.0))ax.set_ylim((0, 2.0))ax.set_xticks(np.arange(0, 2.2, step=0.2))ax.set_yticks(np.arange(0, 2.2, step=0.2))
#设置刻度间隔# x_major_locator=MultipleLocator(.5)# #把x轴的刻度间隔设置为.5，并存在变量里# y_major_locator=MultipleLocator(.5)# ax.xaxis.set_major_locator(x_major_locator)# #把x轴的主刻度设置为.5的倍数# ax.yaxis.set_major_locator(y_major_locator)
for spine in ['top','left','right']:    ax.spines[spine].set_visible(None)ax.spines['bottom'].set_color('k')ax.tick_params(bottom=True,direction='out',labelsize=14,width=1.5,length=4,              left=False)#ax.tick_params()#添加题目titlefontdict = {"size":20,"color":"k",}ax.set_title('Scatter plot of True data and Model Estimated',titlefontdict,pad=20)#ax.set_title()fontdict = {"size":16,"color":"k",'weight':'bold'}ax.text(0.1,1.8,r'$R^2=$'+str(round(C,3)),fontdict=fontdict)ax.text(0.1,1.6,"RMSE="+str(rmse),fontdict=fontdict)ax.text(0.1,1.4,r'$y=$'+str(round(A1,3))+'$x$'+" + "+str(round(B1,3)),fontdict=fontdict)ax.text(0.1,1.2,r'$N=$'+ str(N),fontdict=fontdict)
#添加上下线的统计个数text_font = {'size':'15','weight':'medium','color':'black'}label_font = {'size':'22','weight':'medium','color':'black'}ax.text(.9,.9,"(a)",transform = ax.transAxes,fontdict=text_font,zorder=4)
ax.text(.7,.25,s='Within EE = ' + '{:.0%}'.format(bottom_top_counts/all_data),transform = ax.transAxes,        ha='left', va='center',fontdict=text_font)ax.text(.7,.18,s='Above EE = ' + '{:.0%}'.format(top_counts/all_data),transform = ax.transAxes,        ha='left', va='center',fontdict=text_font)ax.text(.7,.11,s='Below EE = ' + '{:.0%}'.format(bottom_counts/all_data),transform = ax.transAxes,        ha='left', va='center',fontdict=text_font)
ax.text(.8,.056,'nVisualization by DataCharm',transform = ax.transAxes,        ha='center', va='center',fontsize = 10,color='black')# plt.savefig(r'E:Data_resoursesDataCharm 公众号Python学术图表绘制scatter_EE.png',#             width=7,height=4,dpi=900,bbox_inches='tight')plt.show()