def compute_cv(self, Tmin=.01, Tmax=3., nT=100):
T = np.arange(Tmin, Tmax, (Tmax - Tmin) / nT)
Cv = compute_cv_c(np.array(self.ns.max_energies),
float(self.nproc),
float(self.ndof),
T.min(),
T.max(),
T.size,
float(self.ndof),
live=False)
return T, Cv
def compute_heat_capacity(energies, nreplicas, npar=1, ndof=0, Tmin=.1, Tmax=1., nT=100, live_replicas=False):
"""compute the heat capacity and average energies from the energy levels of a nested sampling run
Parameters
----------
energies: nparray of floats
the array of energy levels which is the main result of a nested sampling run. These are stored in
NestedSampling.max_energies
nreplicas : int
npar : int
the number of processors uses in a the nested sampling run. This changes the phase space compression factor
between the energies.
ndof : int
number of degrees of freedom in the system being studied.
This is used to add in the contribution from the momentum degrees of freedom.
If this is not passed the heat capacity will be off by an additive constant.
Tmin, Tmax : float
minimum and maximum temperatures
nT : int
number of temperatures at which to compute the heat capacity
live_replicas : bool
if True then the last nreplcas energies are the sorted energies of the live replicas
at the end of the nested sampling run.
Returns
-------
T : array of temperatures
Cv : array of heat capacities (including the momentum degrees of freedom)
U : the mean energy
U : the mean of the (energy squared)
"""
T, Cv, U, U2 = compute_cv_c(energies, float(npar),
float(nreplicas), float(Tmin), float(Tmax), int(nT),
float(ndof), bool(live_replicas))
Ret = namedtuple("CvReturn", "T Cv U U2")
return Ret(T=T, Cv=Cv, U=U, U2=U2)
def compute_cv(self, Tmin=.01, Tmax=3., nT=100):
T = np.arange(Tmin, Tmax, (Tmax - Tmin) / nT)
Cv = compute_cv_c(np.array(self.ns.max_energies),
float(self.nproc), float(self.ndof), T.min(), T.max(), T.size, float(self.ndof), live=False)
return T, Cv
