def __get_data_corr(self, df_1, df_2, move_len):
#存在数据为nan和inf的，去掉
pd_two = pd.concat([df_1, df_2], axis=1, sort=True)
pd_two = pd_two.replace([np.inf, -np.inf], np.nan).dropna()
item_list_00 = self.__get_best_corr(pd_two, move_len) #获取最优person平移
item_list_00 += self.__time_diff_corr_new(pd_two, move_len) #获取最优TS
item_list_00 += self.__k_l_info(pd_two, move_len) #获取最优KL
#0,3,6位移TS
for i in [0, 3, 6]:
item_list_00 += self.__get_per_ts_kl(
pd.concat([df_1, df_2.shift(i)], axis=1, sort=True))
#获取显著的3个周期频率
freqs_list_1 = []
freqs_list_2 = []
base_cycle_1 = cycle_tool.Cycle_PF(df_1, 1) #周期
freqs_list_1 = base_cycle_1.get_fft_top()[0]
try:
base_cycle_2 = cycle_tool.Cycle_PF(df_2, 1) #周期
freqs_list_2 = base_cycle_2.get_fft_top()[0]
except:
print("err：fft高斯滤波错误", df_2)
item_list_00 += [str(freqs_list_1), str(freqs_list_2)]
return item_list_00
def __fft_gau(self, df):
try:
base_cycle = cycle_tool.Cycle_PF(df, 1) #周期
freqs_list = base_cycle.get_fft_top()[0]
#base_cycle.show_fft()
#base_cycle.get_fft_top()
#三周期拟合数据
base_fit_42_gau = base_cycle.cycle_fit_by_gau(*freqs_list)
except:
print("err：fft高斯滤波错误")
base_fit_42_gau = pd.Series()
return base_fit_42_gau
def do_some():
name = "CBA02701.CS_month.xlsx"
pf = pd.read_excel(name)
pf = pf.set_index("date")
print(pf)
pf_hp = hp_lb_c(pf[pf.columns.tolist()[0]])
plt.show()
raise
sz_cycle = cycle_tool.Cycle_PF(pf_hp, 1)
sz_cycle.get_fft_top()
sz_fft = sz_cycle.show_fft()
freqs_list = sz_cycle.get_fft_top()[0]
base_fit_42_gau = sz_cycle.cycle_fit_by_gau(45)