def perm_M(x, y, w=4, t=1):
"""Permutation mutual information
Creates symbolic timeseries from x and y and calculates the mutual information
"""
s1 = make_permutation_symbols(x, w=w, t=t)
s2 = make_permutation_symbols(y, w=w, t=t)
return M(s1, s2, symbols="w%i" % w)
def perm_M(x,y,w=4,t=1):
"""Permutation mutual information
Creates symbolic timeseries from x and y and calculates the mutual information
"""
s1 = make_permutation_symbols(x,w=w,t=t)
s2 = make_permutation_symbols(y,w=w,t=t)
return M(s1,s2,symbols="w%i"%w)
def perm_D(x, y, w=4, t=1):
"""Metric based on mutual information. Normalized to [0,1]
See http://en.wikipedia.org/wiki/Mutual_information#Metric
"""
s1 = make_permutation_symbols(x, w=w, t=t)
s2 = make_permutation_symbols(y, w=w, t=t)
je = joint_entropy(s1, s2, symbols="w%i" % w)
mi = M(s1, s2, symbols="w%i" % w)
return (je - mi) / je
def perm_D(x,y,w=4,t=1):
"""Metric based on mutual information. Normalized to [0,1]
See http://en.wikipedia.org/wiki/Mutual_information#Metric
"""
s1 = make_permutation_symbols(x,w=w,t=t)
s2 = make_permutation_symbols(y,w=w,t=t)
je = joint_entropy(s1,s2,symbols="w%i"%w)
mi = M(s1,s2,symbols="w%i"%w)
return (je-mi)/je
def perm_TE_ensemble(y, x, w=4, tau=1, test_surr=False):
"""Permutation transfer entropy of two sets of timeseries x and y
Measures information transfer from y to x.
Parameters:
y: 2d-array, first index timepoints, second index ensemble
x: equal-size array
w: word-size for creation of permutation-symbols
tau: time-delay for creation of permutation-symbols
test_surr: bool. If a surrogate-test should be perfomed or not
Returns: 1d-array of length y.shape[0]-1"""
s1 = make_permutation_symbols(y, w=w, t=tau)
s2 = make_permutation_symbols(x, w=w, t=tau)
return TE_ensemble(s1, s2)
def perm_TE_ensemble(y,x,w=4,tau=1,test_surr=False):
"""Permutation transfer entropy of two sets of timeseries x and y
Measures information transfer from y to x.
Parameters:
y: 2d-array, first index timepoints, second index ensemble
x: equal-size array
w: word-size for creation of permutation-symbols
tau: time-delay for creation of permutation-symbols
test_surr: bool. If a surrogate-test should be perfomed or not
Returns: 1d-array of length y.shape[0]-1"""
s1 = make_permutation_symbols(y,w=w,t=tau)
s2 = make_permutation_symbols(x,w=w,t=tau)
return TE_ensemble(s1, s2)
def perm_entropy(x, w=4, t=1, normalize=True):
"""Calculate the permutation-entropy of a time-series x."""
pe = entropy(make_permutation_symbols(x, w, t))
if normalize:
pe = pe / N.log(factorial(w))
return pe
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_entropy(x,w=4,t=1,normalize=True):
"""Calculate the permutation-entropy of a time-series x."""
pe = entropy(make_permutation_symbols(x,w,t))
if normalize:
pe=pe/N.log(factorial(w))
return pe
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))