def j_prob(x, y, z=None, symbols=None):
"""The joint probability of the symbols in x and y"""
#print "symbls", symbols
#extract variables from args
#print args
if z == None:
assert len(x) == len(y), "Sequences must have same length"
else:
assert len(x) == len(y) and len(x) == len(
z), "Sequences must have same length"
#Which symbols exist
if symbols == None:
symbols = np.unique(np.r_[x, y])
elif type(symbols) == str:
symbols = make_all_permutation_symbols(symbols)
if z == None:
ps = N.zeros((len(symbols), len(symbols)), "d")
ks = list(symbols)
ks.sort()
idx_dict = dict([(k, ks.index(k)) for k in ks])
for i in range(len(x)):
ps[idx_dict[x[i]], idx_dict[y[i]]] += 1
ps /= x.shape[0]
else:
ps = N.zeros((len(symbols), len(symbols), len(symbols)), "d")
ks = list(symbols)
ks.sort()
idx_dict = dict([(k, ks.index(k)) for k in ks])
for i in range(len(x)):
ps[idx_dict[x[i]], idx_dict[y[i]], idx_dict[z[i]]] += 1
ps /= x.shape[0]
return ps
def j_prob(x,y,z=None,symbols=None):
"""The joint probability of the symbols in x and y"""
#print "symbls", symbols
#extract variables from args
#print args
if z==None:
assert len(x) == len(y), "Sequences must have same length"
else:
assert len(x) == len(y) and len(x) == len(z), "Sequences must have same length"
#Which symbols exist
if symbols==None:
symbols = np.unique(np.r_[x,y])
elif type(symbols) == str:
symbols = make_all_permutation_symbols(symbols)
if z==None:
ps = N.zeros((len(symbols),len(symbols)),"d")
ks = list(symbols)
ks.sort()
idx_dict = dict([(k,ks.index(k)) for k in ks])
for i in range(len(x)):
ps[idx_dict[x[i]],idx_dict[y[i]]] +=1
ps /= x.shape[0]
else:
ps = N.zeros((len(symbols),len(symbols),len(symbols)),"d")
ks = list(symbols)
ks.sort()
idx_dict = dict([(k,ks.index(k)) for k in ks])
for i in range(len(x)):
ps[idx_dict[x[i]],idx_dict[y[i]],idx_dict[z[i]]] +=1
ps /= x.shape[0]
return ps
def prob(x, symbols=None):
"""Calculate the probabilities for each symbol"""
#print x
if symbols == None:
symbols = np.unique(x)
elif type(symbols) == str:
symbols = make_all_permutation_symbols(symbols)
symbdict = dict([(k, 0) for k in symbols])
for xi in x:
symbdict[xi] += 1
ks = symbols
ks.sort()
ps = np.array([symbdict[k] for k in ks]).astype(np.float)
ps /= x.shape[0]
return ps
def prob(x,symbols=None):
"""Calculate the probabilities for each symbol"""
#print x
if symbols==None:
symbols = np.unique(x)
elif type(symbols) == str:
symbols = make_all_permutation_symbols(symbols)
symbdict = dict([(k,0) for k in symbols])
for xi in x:
symbdict[xi] += 1
ks = symbols
ks.sort()
ps = np.array([symbdict[k] for k in ks]).astype(np.float)
ps /= x.shape[0]
return ps
def perm_TE(y, x, w=4, t=1):
"""Creates symbolic timeseries from real data and calculates Transfer Entropy"""
s1 = make_permutation_symbols(x, w=w, t=t)
s2 = make_permutation_symbols(y, w=w, t=t)
#return TE(s2,s1,symbols="w%i"%w)
return TE(s2, s1, symbols=make_all_permutation_symbols(w))
def perm_TE(y,x,w=4,t=1):
"""Creates symbolic timeseries from real data and calculates Transfer Entropy"""
s1 = make_permutation_symbols(x,w=w,t=t)
s2 = make_permutation_symbols(y,w=w,t=t)
#return TE(s2,s1,symbols="w%i"%w)
return TE(s2,s1,symbols=make_all_permutation_symbols(w))
