def total_correlation(Xs):
pmf = []
Xs = np.asarray(Xs)
for stock in range(len(Xs)):
s = pd.Series(Xs[stock])
b = (s.groupby(s).transform('count') / len(s)).values
pmf.append(b)
pmf = np.asarray(pmf)
pmf /= pmf.sum()
custm = stats.rv_discrete(name='custm', values=(Xs, pmf))
d = Distribution.from_ndarray(pmf)
t = T(d)
return t
label='LEFT')
h_r = ax.hist(seg_t2_r.flatten(),
100,
range=(0, 100),
histtype=u'step',
alpha=1,
lw=2,
label='RIGHT')
ax.set_xlabel('T2 (ms)')
ax.set_ylabel('# COUNTS')
ax.legend()
plt.draw()
plt.savefig(join(joint_save_folder, 'T2_hist.png'), format='png', dpi=300)
# Compute Jensen-Shannon divergence
pmf_l = np.divide(h_l[0], np.sum(h_l[0]))
pmf_r = np.divide(h_r[0], np.sum(h_r[0]))
d_l = Distribution.from_ndarray(pmf_l)
d_r = Distribution.from_ndarray(pmf_r)
JSD = jensen_shannon_divergence([d_l, d_r])
pd_header = ['#counts', 'JS_Divergence']
pd_list = [[np.sum(h_l[0]), JSD], [np.sum(h_r[0]), JSD]]
df = pd.DataFrame(pd_list, columns=pd_header)
df.to_csv(join(joint_save_folder, 'histogram_calculation.csv'))
mat = np.loadtxt('/Users/jk/Desktop/KPAGL/freqs.txt')
output = np.zeros((16, 26))
for i in range(16):
print(i)
freq = mat[i, ]
aa = freq / np.sum(freq)
dat = np.reshape(aa, (4, 4, 3))
dist1 = Distribution.from_ndarray(dat)
p = PID_BROJA(dist1, [[0], [1]], [2])
output[i, 0:4] = [
p.get_partial(n)
for n in sorted(p._lattice, key=dit.pid.lattice.sort_key(p._lattice))
]
p = PID_WB(dist1, [[0], [1]], [2])
output[i, 4:8] = [
p.get_partial(n)
for n in sorted(p._lattice, key=dit.pid.lattice.sort_key(p._lattice))
]
