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')
