本文整理汇总了Python中pymbar.MBAR.computeMultipleExpectations方法的典型用法代码示例。如果您正苦于以下问题：Python MBAR.computeMultipleExpectations方法的具体用法？Python MBAR.computeMultipleExpectations怎么用？Python MBAR.computeMultipleExpectations使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类pymbar.MBAR的用法示例。
在下文中一共展示了MBAR.computeMultipleExpectations方法的4个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: test_mbar_computeMultipleExpectations
# 需要导入模块: from pymbar import MBAR [as 别名]
# 或者: from pymbar.MBAR import computeMultipleExpectations [as 别名]
def test_mbar_computeMultipleExpectations():
"""Can MBAR calculate E(u_kn)??"""
for system_generator in system_generators:
name, test = system_generator()
x_n, u_kn, N_k_output, s_n = test.sample(N_k, mode='u_kn')
eq(N_k, N_k_output)
mbar = MBAR(u_kn, N_k)
A = np.zeros([2,len(x_n)])
A[0,:] = x_n
A[1,:] = x_n**2
state = 1
mu, sigma, covariances = mbar.computeMultipleExpectations(A,u_kn[state,:])
mu0 = test.analytical_observable(observable = 'position')[state]
mu1 = test.analytical_observable(observable = 'position^2')[state]
z = (mu0 - mu[0]) / sigma[0]
eq(z / z_scale_factor, 0*z, decimal=0)
z = (mu1 - mu[1]) / sigma[1]
eq(z / z_scale_factor, 0*z, decimal=0)示例2: enumerate
# 需要导入模块: from pymbar import MBAR [as 别名]
# 或者: from pymbar.MBAR import computeMultipleExpectations [as 别名]
A_kn_all[observe] = A_kn
A_k_estimated_all[observe] = A_k_estimated
A_kl_estimated_all[observe] = A_kl_estimated
print "============================================="
print " Testing computeMultipleExpectations"
print "============================================="
# have to exclude the potential and RMS displacemet for now, not functions of a single state
observables_single = ['position','position^2']
A_ikn = numpy.zeros([len(observables_single), K, N_k.max()], numpy.float64)
for i,observe in enumerate(observables_single):
A_ikn[i,:,:] = A_kn_all[observe]
for i in range(K):
[A_i,dA_ij,Ca_ij] = mbar.computeMultipleExpectations(A_ikn, u_kln[:,i,:], compute_covariance=True)
print "Averages for state %d" % (i)
print A_i
print "Uncertainties for state %d" % (i)
print dA_ij
print "Correlation matrix between observables for state %d" % (i)
print Ca_ij
print "============================================"
print " Testing computeEntropyAndEnthalpy"
print "============================================"
(Delta_f_ij, dDelta_f_ij, Delta_u_ij, dDelta_u_ij, Delta_s_ij, dDelta_s_ij) = mbar.computeEntropyAndEnthalpy(u_kn = u_kln, verbose = True)
print "Free energies"
print Delta_f_ij
print dDelta_f_ij示例3: enumerate
# 需要导入模块: from pymbar import MBAR [as 别名]
# 或者: from pymbar.MBAR import computeMultipleExpectations [as 别名]
# save up the A_k for use in computeMultipleExpectations
A_kn_all[observe] = A_kn
A_k_estimated_all[observe] = A_k_estimated
print "============================================="
print " Testing computeMultipleExpectations"
print "============================================="
# have to exclude the potential and RMS displacemet for now, not functions of a single state
observables_single = ['position','position^2']
A_ikn = numpy.zeros([len(observables_single), K, N_k.max()], numpy.float64)
for i,observe in enumerate(observables_single):
A_ikn[i,:,:] = A_kn_all[observe]
for i in range(K):
[A_i,d2A_ij] = mbar.computeMultipleExpectations(A_ikn, u_kln[:,i,:])
print "Averages for state %d" % (i)
print A_i
print "Correlation matrix between observables for state %d" % (i)
print d2A_ij
print "============================================"
print " Testing computeEntropyAndEnthalpy"
print "============================================"
(Delta_f_ij, dDelta_f_ij, Delta_u_ij, dDelta_u_ij, Delta_s_ij, dDelta_s_ij) = mbar.computeEntropyAndEnthalpy(verbose = True)
print "Free energies"
print Delta_f_ij
print dDelta_f_ij
diffs1 = Delta_f_ij - Delta_f_ij_estimated
print "maximum difference between values computed here and in computeFreeEnergies is %g" % (numpy.max(diffs1))示例4: print
# 需要导入模块: from pymbar import MBAR [as 别名]
# 或者: from pymbar.MBAR import computeMultipleExpectations [as 别名]
A_kn_all[observe] = A_kn
A_k_estimated_all[observe] = A_k_estimated
A_kl_estimated_all[observe] = A_kl_estimated
print("=============================================")
print(" Testing computeMultipleExpectations")
print("=============================================")
# have to exclude the potential and RMS displacemet for now, not functions of a single state
observables_single = ['position','position^2']
A_ikn = numpy.zeros([len(observables_single), K, N_k.max()], numpy.float64)
for i,observe in enumerate(observables_single):
A_ikn[i,:,:] = A_kn_all[observe]
for i in range(K):
results = mbar.computeMultipleExpectations(A_ikn, u_kln[:,i,:], compute_covariance=True)
A_i = results['mu']
dA_ij = results['sigma']
Ca_ij = results['covariances']
print("Averages for state %d" % (i))
print(A_i)
print("Uncertainties for state %d" % (i))
print(dA_ij)
print("Correlation matrix between observables for state %d" % (i))
print(Ca_ij)
print("============================================")
print(" Testing computeEntropyAndEnthalpy")
print("============================================")
results = mbar.computeEntropyAndEnthalpy(u_kn = u_kln, verbose = True)