import matplotlib.pyplot as plt
import numpy as np
# plt.rcParams['font.family'] = 'SimHei' # 指定使用中文字体，例如宋体（SimHei）def Curve_Fitting(x, y, deg):parameter = np.polyfit(x, y, deg)    #拟合deg次多项式p = np.poly1d(parameter)             #拟合deg次多项式aa=''                               #方程拼接  ——————————————————for i in range(deg+1): bb=round(parameter[i], 2)if bb>0:if i==0:bb=str(bb)else:bb='+'+str(bb)else:bb=str(bb)if deg==i:aa=aa+bbelse:aa=aa+bb+'x^'+str(deg-i)    #方程拼接  ——————————————————# 利用相关系数矩阵计算R方r2 = round(np.corrcoef(y, p(x))[0,1]**2,2)plt.scatter(x, y, label="Comparison", color='b', marker='o')     #原始数据散点图plt.plot(x, p(x), color='r')  # 画拟合曲线# plt.text(-1,0,aa,fontdict={'size':'10','color':'b'})plt.legend([f"R^2: {r2}", f"Fitting Equation: {aa}"])   #拼接好的方程和R方放到图例# 添加标签和标题plt.xlabel("Old Normalized_TMB Values")plt.ylabel("New Normalized_TMB Values")plt.title("Comparison of Normalized_TMB")plt.savefig('./tmp.jpg')plt.show()print('曲线方程为：',aa)print('     r^2为：', r2)def plot_scatter(old_list, new_list):# 示例数据：相同样本的两次运行结果# sample_names = ["Sample 1", "Sample 2", "Sample 3", "Sample 4"]run1_values = old_list  # 第一次运行的数据run2_values = new_list  # 第二次运行的数据# 创建散点图plt.scatter(run1_values, run2_values, label="Comparison", color='b', marker='o')# 添加标签和标题plt.xlabel("Old Normalized_TMB Values")plt.ylabel("New Normalized_TMB Values")plt.title("Comparison of Normalized_TMB")# 添加数据点的标签# for i, sample_name in enumerate(sample_names):#     plt.annotate(sample_name, (run1_values[i], run2_values[i]))# 添加一条线表示一对一的关系plt.plot([min(run1_values), max(run1_values)], [min(run1_values), max(run1_values)], linestyle='--', color='r', label="1:1 Line")# 显示图例plt.legend()plt.savefig('./tmp.jpg')# 显示散点图plt.show()normalzed_tmb_compare_file = 'normalzed_tmb.txt'
old_FFPE_list, new_FFPE_list = [], []
old_CF_list, new_CF_list = [], []
with open(normalzed_tmb_compare_file) as f:next(f)for line in f:line_list = line.strip('\n').split('\t')# if float(line_list[0]) > 20:#     continueold, type_, new = line_listif type_ == 'FFPE':old_FFPE_list.append(float(line_list[0]))new_FFPE_list.append(float(line_list[-1]))if type_ == 'CF':old_CF_list.append(float(line_list[0]))new_CF_list.append(float(line_list[-1])) # Curve_Fitting(old_FFPE_list, new_FFPE_list, 1)# Curve_Fitting(old_CF_list, new_CF_list, 1)Curve_Fitting(old_CF_list+old_FFPE_list, new_CF_list+new_FFPE_list, 1)# plot_scatter(old_list, new_list)