def calc_Cv(self, NDOF):
nebins = self.nebins
visits1d = self.visits2d.sum(2)
logn_E = np.zeros(nebins)
for i in range(nebins):
logn_E[i] = wham_utils.logSum(self.logn_Eq[i,:])
return wham_utils.calc_Cv(logn_E, visits1d, self.binenergy, \
NDOF, self.Tlist, self.k_B)
def calc_Cv(self, NDOF):
nebins = self.nebins
visits1d = self.visits2d.sum(2)
logn_E = np.zeros(nebins)
for i in range(nebins):
logn_E[i] = wham_utils.logSum(self.logn_Eq[i,:])
return wham_utils.calc_Cv(logn_E, visits1d, self.binenergy, \
NDOF, self.Tlist, self.k_B)
def calc_Fq(self, TRANGE = []):
self.allzero2dind = np.where(self.visits2d.sum(0) == 0)
#put some variables in this namespace
nebins=self.nebins
nqbins=self.nqbins
binenergy=self.binenergy
visits2d=self.visits2d
logn_Eq=self.logn_Eq
if len(TRANGE) == 0:
NTEMP = 5 # number of temperatures to calculate expectation values
TMAX = self.Tlist[-1]
TMIN = self.Tlist[0]
TINT=(TMAX-TMIN)/(NTEMP-1)
TRANGE = [ TMIN + i*TINT for i in range(NTEMP) ]
#find the occupied bin with the minimum energy
EREF=0
for i in range(nebins):
if visits2d[:,i,:].sum() > 0:
EREF = binenergy[i]
break
self.nodataq = np.where((visits2d.sum(0).sum(0)) == 0)
#now calculate P(q,T)
# P(q,T) = sum_E n(E,q)*exp(-E/T)
#TRANGE=range(1,9)
self.F_q = np.zeros([nqbins,len(TRANGE)])
F_q = self.F_q
logP_Eq = np.zeros([nebins,nqbins])
logP_q = np.zeros(nqbins)
for n in range(len(TRANGE)):
T=TRANGE[n]
for i in range(nebins):
logP_Eq[i,:] = logn_Eq[i,:]-(binenergy[i] - EREF)/(self.k_B*T)
logP_Eq[self.allzero2dind[0], self.allzero2dind[1]] = self.LOGMIN
expoffset = np.nanmax(logP_Eq)
#print "T expoffset ", T, expoffset
logP_Eq -= expoffset
#P_q = np.exp(logP_Eq).sum(0)
# sum over the energy
for j in range(nqbins):
logP_q[j] = logSum( logP_Eq[:,j] )
logP_q[self.nodataq] = np.NaN
F_q[:,n] = -self.k_B*T*logP_q[:]
fmin = np.nanmin(F_q[:,n])
F_q[:,n] -= fmin
return TRANGE,F_q
def calc_Fq(self, TRANGE = []):
self.allzero2dind = np.where(self.visits2d.sum(0) == 0)
#put some variables in this namespace
nebins=self.nebins
nqbins=self.nqbins
binenergy=self.binenergy
visits2d=self.visits2d
logn_Eq=self.logn_Eq
if len(TRANGE) == 0:
NTEMP = 5 # number of temperatures to calculate expectation values
TMAX = self.Tlist[-1]
TMIN = self.Tlist[0]
TINT=(TMAX-TMIN)/(NTEMP-1)
TRANGE = [ TMIN + i*TINT for i in range(NTEMP) ]
#find the occupied bin with the minimum energy
EREF=0
for i in range(nebins):
if visits2d[:,i,:].sum() > 0:
EREF = binenergy[i]
break
self.nodataq = np.where((visits2d.sum(0).sum(0)) == 0)
#now calculate P(q,T)
# P(q,T) = sum_E n(E,q)*exp(-E/T)
#TRANGE=range(1,9)
self.F_q = np.zeros([nqbins,len(TRANGE)])
F_q = self.F_q
logP_Eq = np.zeros([nebins,nqbins])
logP_q = np.zeros(nqbins)
for n in range(len(TRANGE)):
T=TRANGE[n]
for i in range(nebins):
logP_Eq[i,:] = logn_Eq[i,:]-(binenergy[i] - EREF)/(self.k_B*T)
logP_Eq[self.allzero2dind[0], self.allzero2dind[1]] = self.LOGMIN
expoffset = np.nanmax(logP_Eq)
#print "T expoffset ", T, expoffset
logP_Eq -= expoffset
#P_q = np.exp(logP_Eq).sum(0)
# sum over the energy
for j in range(nqbins):
logP_q[j] = logSum( logP_Eq[:,j] )
logP_q[self.nodataq] = np.NaN
F_q[:,n] = -self.k_B*T*logP_q[:]
fmin = np.nanmin(F_q[:,n])
F_q[:,n] -= fmin
return TRANGE,F_q
def calc_qavg(self, TRANGE = []):
"""calculate the average q as a function of temperature"""
#put some variables in this namespace
nebins=self.nebins
nqbins=self.nqbins
binenergy=self.binenergy
binq=self.binq
visits2d=self.visits2d
logn_Eq=self.logn_Eq
if len(TRANGE) == 0:
NTEMP = 100 # number of temperatures to calculate expectation values
TMAX = self.Tlist[-1]
TMIN = self.Tlist[0]
TINT=(TMAX-TMIN)/(NTEMP-1)
TRANGE = [ TMIN + i*TINT for i in range(NTEMP) ]
#find the ocupied bin with the minimum energy
EREF=0
for i in range(nebins):
if visits2d[:,i,:].sum() > 0:
EREF = binenergy[i]
break
#don't need to recalculate it
#self.nodataq = where((visits2d.sum(2).sum(0)) == 0)
#calculate the mean q at each temperature
self.qavg = np.zeros(len(TRANGE))
#now calculate P(q,T)
# P(q,T) = sum_E n(E,q)*exp(-E/T)
#TRANGE=range(1,9)
logP_Eq = np.zeros([nebins,nqbins])
logP_q = np.zeros(nqbins)
for n in range(len(TRANGE)):
T=TRANGE[n]
for i in range(nebins):
logP_Eq[i,:] = logn_Eq[i,:]-(binenergy[i] - EREF)/(self.k_B*T)
logP_Eq[self.allzero2dind[0], self.allzero2dind[1]] = self.LOGMIN
expoffset = logP_Eq.max()
#print "T expoffset ", T, expoffset
logP_Eq -= expoffset
#P_q = np.exp(logP_Eq).sum(0)
# sum over the energy
for j in range(nqbins):
logP_q[j] = wham_utils.logSum( logP_Eq[:,j] )
logP_q[self.nodataq] = np.NaN
#find mean q
qmin = min(binq)
qmin -= 0.1
lqavg = -1.0e30
lnorm = -1.0e30
for i in range(0,nqbins):
if not np.isnan(logP_q[i]):
lnorm = wham_utils.logSum1( lnorm, logP_q[i] )
lqavg = wham_utils.logSum1( lqavg, logP_q[i] + log(binq[i] - qmin) )
self.qavg[n] = exp(lqavg - lnorm) + qmin
#print lqavg
return TRANGE,self.qavg
def calc_qavg(self, TRANGE = []):
"""calculate the average q as a function of temperature"""
#put some variables in this namespace
nebins=self.nebins
nqbins=self.nqbins
binenergy=self.binenergy
binq=self.binq
visits2d=self.visits2d
logn_Eq=self.logn_Eq
if len(TRANGE) == 0:
NTEMP = 100 # number of temperatures to calculate expectation values
TMAX = self.Tlist[-1]
TMIN = self.Tlist[0]
TINT=(TMAX-TMIN)/(NTEMP-1)
TRANGE = [ TMIN + i*TINT for i in range(NTEMP) ]
#find the ocupied bin with the minimum energy
EREF=0
for i in range(nebins):
if visits2d[:,i,:].sum() > 0:
EREF = binenergy[i]
break
#don't need to recalculate it
#self.nodataq = where((visits2d.sum(2).sum(0)) == 0)
#calculate the mean q at each temperature
self.qavg = np.zeros(len(TRANGE))
#now calculate P(q,T)
# P(q,T) = sum_E n(E,q)*exp(-E/T)
#TRANGE=range(1,9)
logP_Eq = np.zeros([nebins,nqbins])
logP_q = np.zeros(nqbins)
for n in range(len(TRANGE)):
T=TRANGE[n]
for i in range(nebins):
logP_Eq[i,:] = logn_Eq[i,:]-(binenergy[i] - EREF)/(self.k_B*T)
logP_Eq[self.allzero2dind[0], self.allzero2dind[1]] = self.LOGMIN
expoffset = logP_Eq.max()
#print "T expoffset ", T, expoffset
logP_Eq -= expoffset
#P_q = np.exp(logP_Eq).sum(0)
# sum over the energy
for j in range(nqbins):
logP_q[j] = wham_utils.logSum( logP_Eq[:,j] )
logP_q[self.nodataq] = np.NaN
#find mean q
qmin = min(binq)
qmin -= 0.1
lqavg = -1.0e30
lnorm = -1.0e30
for i in range(0,nqbins):
if not np.isnan(logP_q[i]):
lnorm = wham_utils.logSum1( lnorm, logP_q[i] )
lqavg = wham_utils.logSum1( lqavg, logP_q[i] + log(binq[i] - qmin) )
self.qavg[n] = exp(lqavg - lnorm) + qmin
#print lqavg
return TRANGE,self.qavg
