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')
def plot_pam():
pamSigphi = pd.read_csv(r'../ffm_Phi_1/output/pam_phi_sig.csv',
header=0,
index_col=0)
pamTwopara = pd.read_csv(r'../sep_find_line_4/output/pam_info.csv',
header=0,
index_col=0)
file = os.listdir(r'../input_data_0/pam_mon/')
for i, name in enumerate(file):
# 观测数据
df = pd.read_csv(r'../input_data_0/pam_mon/%s' % name,
header=0,
index_col=0)
e = df.index.values
flux = df.iloc[:, 0]
# 不随能量变化的 phi 计算 出的通量
e_series = np.linspace(0.0085, 50, 1000)
fluxSigpoint = FUNC.ffm_fun_all(e, pamSigphi.iloc[:, 0].values[i])
fluxSigseries = FUNC.ffm_fun_all(e_series, pamSigphi.iloc[:,
0].values[i])
# 模型计算出的 通量
Phi_point_c = FUNC.fit_obj_vary(e, pamTwopara.loc[:,
'phi_0'].values[i],
pamTwopara.loc[:, 'b'].values[i])
fluxModlepoint = FUNC.ffm_fun_all(e, Phi_point_c)
Phi_serier_c = FUNC.fit_obj_vary(e_series,
pamTwopara.loc[:, 'phi_0'].values[i],
pamTwopara.loc[:, 'b'].values[i])
fluxModleseries = FUNC.ffm_fun_all(e_series, Phi_serier_c)
# SSN 计算出的结果
Phi_point_ssn = FUNC.fit_obj_vary(e, pamTwopara.loc[:,
'phi_c'].values[i],
pamTwopara.loc[:, 'b_c'].values[i])
fluxSSNpoint = FUNC.ffm_fun_all(e, Phi_point_ssn)
Phi_series_ssn = FUNC.fit_obj_vary(
e_series, pamTwopara.loc[:, 'phi_c'].values[i],
pamTwopara.loc[:, 'b_c'].values[i])
fluxSSNseries = FUNC.ffm_fun_all(e_series, Phi_series_ssn)
# LIS
flux_lis = FUNC.LIS_5(e_series)
# 误差
sig_err_point = (fluxSigpoint - flux) / flux
mod_err_point = (fluxModlepoint - flux) / flux
ssn_err_point = (fluxSSNpoint - flux) / flux
# 画图
plt.figure(figsize=(8, 6))
plt.style.use('fast')
plt.style.use('seaborn-ticks')
grid = plt.GridSpec(8, 6, hspace=0)
ax_flux = plt.subplot(grid[0:5, 0:6])
plt.scatter(e,
flux,
marker='o',
label='PAMELA',
facecolors='none',
edgecolor='k',
alpha=0.8,
s=40,
zorder=100)
plt.scatter(e,
fluxSSNpoint,
marker='D',
s=15,
facecolors='none',
edgecolor='orange',
alpha=1,
label='FFM',
zorder=101)
plt.plot(e_series,
flux_lis,
'r--',
color='m',
label='LIS',
lw=2.5,
zorder=10,
alpha=0.5)
plt.plot(e_series,
fluxSigseries,
'r-',
color='dodgerblue',
label='FIT',
alpha=0.8,
lw=2.5,
zorder=11)
plt.plot(e_series,
fluxModleseries,
color='lime',
label='MODEL',
alpha=0.8,
lw=2.5,
zorder=1)
ax_err = plt.subplot(grid[5:8, 0:6], sharex=ax_flux)
plt.scatter(e,
ssn_err_point * 100,
marker='D',
facecolor='none',
edgecolors='orange',
label='FFM',
s=15,
alpha=0.8)
plt.scatter(e,
sig_err_point * 100,
marker='o',
facecolor='none',
edgecolors='dodgerblue',
label='FIT',
s=15,
alpha=0.8)
plt.scatter(e,
mod_err_point * 100,
marker='o',
facecolor='none',
edgecolors='lime',
label='MODEL',
s=15,
alpha=0.8)
ax_err.set_ylim(-30, 30)
ax_err.grid(True, ls='-.', which='both', alpha=0.2)
ax_err.tick_params(axis='both', direction='in', which='both')
ax_err.set_ylabel(r'$(F_{pre}-F_{obs})/F_{obs}$ (%)', fontsize='10')
ax_err.legend(loc='upper right')
# ######################################################################
periods_list = list(pamTwopara.index)
ax_flux.set_title('E-Flux Periods: %s' % periods_list[i],
fontsize=10,
weight='bold',
style='italic')
ax_err.set_xlabel('Kinetic Energy (GeV)', fontsize='10')
ax_flux.set_ylabel('Flux $(m^{2}.s.sr.GeV)^{-1}$ ', fontsize='10')
ax_flux.set_xscale('log')
ax_flux.set_yscale('log')
ax_err.set_xlim(0.0885, 115)
ax_flux.set_ylim(0.2, 30000)
ax_flux.tick_params(axis='both', direction='in', which='both')
ax_flux.legend(loc='upper right')
plt.savefig(r'./output/flux/pamela/periods_%s.png' % periods_list[i],
dpi=250)
plt.savefig(r'./output/flux/pamela/gif/periods_%d.png' % i,
dpi=250) # 保存路径;
plt.cla()
plt.close("all")
print('PAMELA %s' % periods_list[i], '完成 ==>')
print('PAMELA 结束')
print(phi_lr)
print(len(phi_lr))
"""
保存LR计算出的phi_lr 计算 flux_lr (log)
"""
df_info_lr = df_info.loc['1977-06-01':'2020-01-01', :]
print(len(df_info_lr))
df_info_lr['phi_lr'] = phi_lr
print(df_info_lr)
df_info_lr.plot(y='phi_lr')
b_c = df_info.loc['1977-06-01':'2020-01-01', 'b_c'].values
Phi_lr = FUNC.fit_obj_vary(0.274, phi_lr, b_c)
flux_lr = FUNC.ffm_fun_all(0.274 * np.ones(len(Phi_lr)), Phi_lr)
# ==============================================================
'''
平滑flux
'''
print('==============================================================')
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,
