def _spear(x1, x2, m):
n = x1.shape[0]
xix = np.empty(n)*np.nan
ny = np.maximum(crtf.fst_nan(x1), crtf.fst_nan(x2))
if ny+m < n:
for i in range(ny+m, n):
xix[i] = _spearman(x1[i-m:i], x2[i-m:i])
xix_ = crtf.fish_inv_trans(crtf.lrbeta(crtf.cumsum_special(crtf.fish_trans(xix)), 7, lg=False))
xix1 = np.empty(n)*np.nan
ny = crtf.fst_nan(xix_)
xix1[ny+7*3:] = xix_[ny+7*3:]
return xix1
else:
return xix
def cum_spec(x):
ny = crtf.fst_nan(x)
n = x.shape[0]
y = np.empty(n)*np.nan
y[ny:n] = np.cumsum(x[ny:n])
return y
#
# ---
x12_corr2 = np.ones(n)*np.nan
x12_corr2 = 3*crtf.ema(x1*x2, mi2)/np.sqrt(crtf.ema(x1**2, mi2)*crtf.ema(x2**2, mi2))-\
2*crtf.ema(x1*x2, mi)/np.sqrt(crtf.ema(x1**2, mi)*crtf.ema(x2**2, mi))
crup.plot_ts(dt, x12_corr2)
x12_corr1_ft = crtf.fish_trans(x12_corr2)
x12_corr1_ft_c = cum_spec(x12_corr1_ft)
# x12_corr1_ft_sm = 6*(crtf.ema(x12_corr1_ft_c, 5)-crtf.ema(x12_corr1_ft_c, 7))/(7-1)
x12_corr1_ft_sm = crtf.lrbeta(x12_corr1_ft_c, 5, lg=False)
x12_corr6_ = crtf.fish_inv_trans(x12_corr1_ft_sm)
ny = crtf.fst_nan(x12_corr6_)
x12_corr6 = np.empty(x12_corr6_.shape[0])*np.nan
x12_corr6[ny+7*2:] = x12_corr6_[ny+7*2:]
crup.plot_ts(dt, x12_corr6)
x12_corr3 = crtf.cor_cc(testc1, testc2, mi)
crup.plot_ts(dt, x12_corr3[xidx])
# import matplotlib.pyplot as plt
# plt.figure(2)
# crup.plot_ts(dt, testc1[xidx])
# plt.figure(3)
# crup.plot_ts(dt, testc2[xidx])
#
#
#