/usr/lib/python3/dist-packages/csb/statmech/ensembles.py is in python3-csb 1.2.2+dfsg-2ubuntu1.
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Statistical Ensembles
"""
from csb.numeric import log, exp
from abc import ABCMeta, abstractmethod
class StatisticalEnsemble(object):
__metaclass__ = ABCMeta
def __call__(self, raw_energies):
return exp(-self.energy(raw_energies))
def log_prob(self, raw_energies):
return -self.energy(raw_energies)
@abstractmethod
def energy(self, raw_energies):
"""
Transforms the raw energies as if they were observed
in this statistical ensemble
"""
pass
def gradient(self, raw_energies):
raise NotImplementedError()
class BoltzmannEnsemble(StatisticalEnsemble):
def __init__(self, beta=1.):
self._beta = float(beta)
@property
def beta(self):
"""
Inverse temperature
"""
return self._beta
@beta.setter
def beta(self, value):
value = float(value)
if value <= 0.:
raise ValueError("Inverse temperature {0} < 0".formate(value))
self._beta = value
def energy(self, raw_energies):
return raw_energies * self._beta
class FermiEnsemble(BoltzmannEnsemble):
def __init__(self, beta=1., e_max=0.):
super(FermiEnsemble, self).__init__(beta)
self._e_max = float(e_max)
@property
def e_max(self):
"""
Maximum energy
"""
return self._e_max
@e_max.setter
def e_max(self, value):
self._e_max = float(value)
def energy(self, raw_energies):
from numpy import isinf
if isinf(self.beta):
m = (raw_energies >= self.e_max).astype('f')
return - m * log(0.)
else:
x = 1 + exp(self.beta * (raw_energies - self.e_max))
return log(x)
class TsallisEnsemble(StatisticalEnsemble):
def __init__(self, q=1., e_min=0.):
self._q = q
self._e_min = e_min
@property
def q(self):
"""
q-analoge of the temperature
"""
return self._q
@q.setter
def q(self, value):
if value <= 0.:
raise ValueError("Inverse temperature {0} < 0".formate(value))
self._q = value
@property
def e_min(self):
"""
lower bound of the energy
"""
return self._e_min
@e_min.setter
def e_min(self, value):
self._e_min = value
def energy(self, raw_energies):
q = self.q
e_min = self.e_min
if (q < 1 + 1e-10):
return raw_energies * q
else:
return log(1 + (raw_energies - e_min) * (q - 1)) * q / (q - 1) + e_min
class CompositeEnsemble(StatisticalEnsemble):
def __init__(self, ensembles=[]):
self._ensembles = ensembles
@property
def ensembles(self):
"""
Collection of statistical ensembles
"""
return self._ensembles
@ensembles.setter
def ensembles(self, value):
if not isinstance(value, list):
if len(value) > 0:
if not isinstance(value[0], StatisticalEnsemble):
raise ValueError("Not a list of statistical ensembles")
else:
self._enesmbles = value
else:
self._enesmbles = value
def energy(self, raw_energies):
return sum([self._ensembles[i].energy(raw_energies[i])
for i in range(len(self.ensembles))], 0)
def gradient(self, raw_energies):
return sum([self._ensembles[i].gradient(raw_energies[i])
for i in range(len(self.ensembles))], 0)
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