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')
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))]
df_pam = df_pam.drop(index=pam_nan_list)
