计算 phi_c ,b_c ,Phi_c, flux_c ''' ssn_in = range_info.loc[:, 'ssn_delay'].values plt.figure(figsize=(8, 4)) plt.plot(range(len(delay_ssn)), delay_ssn, label='Data', c='b', lw=2, zorder=12) plt.plot(range(len(delay_ssn)), ssn_in, label='Smooth', c='r', lw=2) plt.legend() plt.show() phi_c = FUNC.ssn_phi(ssn_in) b_c = FUNC.ssn_b(ssn_in) Phi_c = FUNC.fit_obj_vary(0.274, phi_c, b_c) flux_c = np.array([]) for i in range(len(Phi_c)): flux_sig = FUNC.ffm_fun(0.274, Phi_c[i]) flux_c = np.append(flux_c, flux_sig) # print(range_info) range_info['phi_c'] = phi_c range_info['b_c'] = b_c range_info['Phi_c'] = Phi_c range_info['flux_c'] = flux_c range_info.to_csv(r'./output/info_test.csv')
0].values) y_b_err = np.append(df_pam_err.iloc[:, 1].values, df_ams_err.iloc[:, 1].values) # 计算 ssn-phi/b 的系数 phi_para, _ = curve_fit(FUNC.obj, x_ssn, y_phi, sigma=y_phi_err, absolute_sigma=True) b_para, _ = curve_fit(FUNC.obj, x_ssn, y_b, sigma=y_b_err, absolute_sigma=True) print('phi:', phi_para) print('b:', b_para) # 讲关系式写入FFM 后计算 phi_c ,b_c 并且加载到 phi-b 文件 df_pam['phi_c'] = FUNC.ssn_phi(df_pam.loc[:, 'ssn_del'].values) df_pam['b_c'] = FUNC.ssn_b(df_pam.loc[:, 'ssn_del'].values) df_ams['phi_c'] = FUNC.ssn_phi(df_ams.loc[:, 'ssn_del'].values) df_ams['b_c'] = FUNC.ssn_b(df_ams.loc[:, 'ssn_del'].values) # 删除空缺的时间 并保存到 output 的 info文件 pam_nan = pd.read_csv(r'../double_two_2/output/pamela_mon_nan.csv', header=0, index_col=0) ams_nan = pd.read_csv(r'../double_two_2/output/ams02_mon_nan.csv', header=0, index_col=0) pam_nan_list = [int(pam_nan.iloc[:, 0][i]) for i in range(len(pam_nan))] ams_nan_list = [int(ams_nan.iloc[:, 0][i]) for i in range(len(ams_nan))]