huaxian_ssa.plot_wcorr(max=11)
plt.title("W-Correlation for the monthly Runoff of Huaxian", fontsize=10)
plt.subplots_adjust(left=0.12,
bottom=0.06,
right=0.9,
top=0.98,
hspace=0.4,
wspace=0.25)
# plt.savefig(root_path+'/Huaxian_ssa/graphs/w_correlation.eps',format='EPS',dpi=2000)
# plt.savefig(root_path+'/Huaxian_ssa/graphs/w_correlation.tif',format='TIFF',dpi=1000)
plt.show()
print("@@@@")
#%%
plt.figure()
huaxian_ssa.reconstruct(0).plot()
huaxian_ssa.reconstruct([1, 2]).plot()
huaxian_ssa.reconstruct([3, 4]).plot()
huaxian_ssa.orig_TS.plot(alpha=0.4)
plt.title("Monthly Runoff of Huaxian: First Three groups")
plt.xlabel(r"$t$(month)")
plt.ylabel(r"Runoff($m^3/s$)")
legend = [r"$\tilde{{F}}^{{({0})}}$".format(i)
for i in range(3)] + ["Original TS"]
plt.legend(legend)
#%%
plt.figure()
huaxian_ssa.reconstruct(slice(0, 5)).plot()
huaxian_ssa.orig_TS.plot(alpha=0.4)
plt.title("Monthly Runoff of Huaxian: Low-Frequancy Periodicity")
#%%
F_ssa_L5 = SSA(F, 5)
F_ssa_L5.components_to_df().plot()
F_ssa_L5.orig_TS.plot(alpha=0.4)
plt.xlabel("$t$")
plt.ylabel(r"$\tilde{F}_i(t)$")
plt.title(r"$L=5$ for the Toy Time Series")
#%%
F_ssa_L20 = SSA(F, 20)
F_ssa_L20.plot_wcorr()
plt.title("W-Correlation for Toy Time Series, $L=20$")
#%%
F_ssa_L20.reconstruct(0).plot()
F_ssa_L20.reconstruct([1, 2, 3]).plot()
F_ssa_L20.reconstruct(slice(4, 20)).plot()
F_ssa_L20.reconstruct(3).plot()
plt.xlabel("$t$")
plt.ylabel(r"$\tilde{F}_i(t)$")
plt.title("Component Groupings for Toy Time Series, $L=20$")
plt.legend([
r"$\tilde{F}_0$",
r"$\tilde{F}_1+\tilde{F}_2+\tilde{F}_3$",
r"$\tilde{F}_4+\ldots+\tilde{F}_{19}$",
r"$\tilde{F}_3$",
])
#%%
F_ssa_L40 = SSA(F, 40)
'Periodic2', #F2
'Periodic3', #F3
'Periodic4', #F4
'Periodic5', #F5
'Periodic6', #F6
'Periodic7', #F7
'Periodic8', #F8
'Periodic9', #F9
'Periodic10', #F10
'Noise', #F11
]
#%%
# Decompose the entire monthly runoff of HuaXian
HuaXian_ssa = SSA(full, window)
F0 = HuaXian_ssa.reconstruct(0)
F1 = HuaXian_ssa.reconstruct(1)
F2 = HuaXian_ssa.reconstruct(2)
F3 = HuaXian_ssa.reconstruct(3)
F4 = HuaXian_ssa.reconstruct(4)
F5 = HuaXian_ssa.reconstruct(5)
F6 = HuaXian_ssa.reconstruct(6)
F7 = HuaXian_ssa.reconstruct(7)
F8 = HuaXian_ssa.reconstruct(8)
F9 = HuaXian_ssa.reconstruct(9)
F10 = HuaXian_ssa.reconstruct(10)
F11 = HuaXian_ssa.reconstruct(11)
orig_TS = HuaXian_ssa.orig_TS
df = pd.concat([orig_TS, F0, F1, F2, F3, F4, F5, F6, F7, F8, F9, F10, F11],
axis=1)
df = pd.DataFrame(df.values, columns=columns)
'Periodic2', #F2
'Periodic3', #F3
'Periodic4', #F4
'Periodic5', #F5
'Periodic6', #F6
'Periodic7', #F7
'Periodic8', #F8
'Periodic9', #F9
'Periodic10', #F10
'Noise', #F11
]
#%%
# Decompose the entire monthly runoff of huaxian
huaxian_ssa = SSA(full, window)
F0 = huaxian_ssa.reconstruct(0)
F1 = huaxian_ssa.reconstruct(1)
F2 = huaxian_ssa.reconstruct(2)
F3 = huaxian_ssa.reconstruct(3)
F4 = huaxian_ssa.reconstruct(4)
F5 = huaxian_ssa.reconstruct(5)
F6 = huaxian_ssa.reconstruct(6)
F7 = huaxian_ssa.reconstruct(7)
F8 = huaxian_ssa.reconstruct(8)
F9 = huaxian_ssa.reconstruct(9)
F10 = huaxian_ssa.reconstruct(10)
F11 = huaxian_ssa.reconstruct(11)
orig_TS = huaxian_ssa.orig_TS
df = pd.concat([orig_TS, F0, F1, F2, F3, F4, F5, F6, F7, F8, F9, F10, F11],
axis=1)
df = pd.DataFrame(df.values, columns=columns)
