phi_pos = np.array([FUNC.LR_postive(para[i]) for i in range(len(para))])
Phi_pos = FUNC.fit_obj_vary(0.274, phi_pos, b_c)
flux_pos = FUNC.ffm_fun_all(0.274 * np.ones(len(Phi_pos)), Phi_pos)
phi_neg = np.array([FUNC.LR_negative(para[i]) for i in range(len(para))])
Phi_neg = FUNC.fit_obj_vary(0.274, phi_neg, b_c)
flux_neg = FUNC.ffm_fun_all(0.274 * np.ones(len(Phi_neg)), Phi_neg)
df_flux = pd.DataFrame(np.vstack((flux_pos, flux_neg, Phi_pos, Phi_neg)).T,
index=list(df_info_lr.index), columns=['pos', 'neg', 'phi_pos', 'phi_neg'])
# plt.plot(range(len(Phi_pos)),Phi_lr)
# # plt.plot(range(len(Phi_pos)),Phi_neg)
# # df_flux.plot(y=['pos','neg'])
# plt.show()
# print(df_flux)
p1, p2 = FUNC.sigmod_smooth(24)
# df_flux.to_csv(r'./output/flux.csv')
fp1 = df_flux.loc['1977-12-01':'1981-12-01', 'pos']
fn1 = df_flux.loc['1977-12-01':'1981-12-01', 'neg']
f1 = (fp1 * p1 + fn1 * p2)
fp2 = df_flux.loc['1987-12-01':'1991-12-01', 'pos']
fn2 = df_flux.loc['1987-12-01':'1991-12-01', 'neg']
f2 = (fn2 * p1 + fp2 * p2)
fp3 = df_flux.loc['1998-05-01':'2002-05-01', 'pos']
fn3 = df_flux.loc['1998-05-01':'2002-05-01', 'neg']
f3 = (fp3 * p1 + fn3 * p2)
fp4 = df_flux.loc['2010-06-01':'2014-06-01', 'pos']
