def entropy(X, P, n, precision, truncate, verbose=False):
'''
Using the correlator matrices, find the nth entropy.
:param X: spatial correlator matrix (sympy)
:param P: momentum correlator matrix (sympy)
:param n: Renyi index
:param precision: arithmetic precision
:param verbose: runtime output flag
'''
XP = sp.dot(X, P)
# DEBUGGING: Saving the correlator matrices.
# prec = sympy.mpmath.mp.dps
# sp.savetxt("xmat.txt", X, fmt='%.{0}f'.format(prec),delimiter=",")
# sp.savetxt("pmat.txt",P, fmt='%.{0}f'.format(prec),delimiter=",")
# Scipy eigenvalues
sqrt_eigs = sp.sqrt(linalg.eigvals(XP))
# Check that the eigenvalues are well-defined.
to_remove = []
for i, eig in enumerate(sqrt_eigs):
if eig.real <= 0.5:
if truncate:
# Remove the eigenvalue
to_remove.append(i)
else:
raise ValueError(
"At least one of the eigenvalues of sqrt(XP) is below 0.5! \n eig = {0}"
.format(eig))
if eig.imag != 0:
print "Warning: got an imaginary eigvalue component: " + str(
eig.imag)
sqrt_eigs = sp.delete(sqrt_eigs, to_remove)
# Chop off imaginary component.
sqrt_eigs = sqrt_eigs.real
# Calculate entropy.
S_n = 0
if n == 1:
for vk in sqrt_eigs:
S_n += ((vk + 0.5) * log(vk + 0.5) - (vk - 0.5) * log(vk - 0.5))
else:
for vk in sqrt_eigs:
S_n += log((vk + 0.5)**n - (vk - 0.5)**n)
S_n *= 1. / (n - 1)
if verbose == True:
print "Calculated entropy of {0}".format(S_n)
S_n = float(S_n)
return S_n
def entropy(X, P, n, precision, truncate, verbose=False):
'''
Using the correlator matrices, find the nth entropy.
:param X: spatial correlator matrix (sympy)
:param P: momentum correlator matrix (sympy)
:param n: Renyi index
:param precision: arithmetic precision
:param verbose: runtime output flag
'''
XP = sp.dot(X,P)
# DEBUGGING: Saving the correlator matrices.
# prec = sympy.mpmath.mp.dps
# sp.savetxt("xmat.txt", X, fmt='%.{0}f'.format(prec),delimiter=",")
# sp.savetxt("pmat.txt",P, fmt='%.{0}f'.format(prec),delimiter=",")
# Scipy eigenvalues
sqrt_eigs = sp.sqrt(linalg.eigvals(XP))
# Check that the eigenvalues are well-defined.
to_remove = []
for i, eig in enumerate(sqrt_eigs):
if eig.real <= 0.5:
if truncate:
# Remove the eigenvalue
to_remove.append(i)
else:
raise ValueError("At least one of the eigenvalues of sqrt(XP) is below 0.5! \n eig = {0}".format(eig))
if eig.imag != 0:
print "Warning: got an imaginary eigvalue component: " + str(eig.imag)
sqrt_eigs = sp.delete(sqrt_eigs,to_remove)
# Chop off imaginary component.
sqrt_eigs = sqrt_eigs.real
# Calculate entropy.
S_n = 0
if n == 1:
for vk in sqrt_eigs:
S_n += ((vk + 0.5)*log(vk + 0.5) - (vk - 0.5)*log(vk - 0.5))
else:
for vk in sqrt_eigs:
S_n += log((vk + 0.5)**n - (vk - 0.5)**n)
S_n *= 1./(n-1)
if verbose==True:
print "Calculated entropy of {0}".format(S_n)
S_n = float(S_n)
return S_n
def test_log():
"""
Se verifica que la operacion logaritmo funcione.
"""
num, num2 = TwoReals()
try:
a = mp.log(num)
b = mp.log(num2)
c = Intervalo(num, num2).log()
assert a == c.lo and b == c.hi
except:
if num2 < 0:
pass
#Debemos checar que se de el ValueError
elif num < 0 and num2 >= 0:
a = mp.log(num + np.abs(num))
b = mp.log(num2)
c = Intervalo(num, num2).log()
assert a == c.lo and b == c.hi
def eval(self, *args):
return mpmath.log(args[1], args[0])
def eval(self, *args):
return mpmath.log(args[1], args[0])
