def _calc_singleLP_exTI_pred_mbar(Es, dEs, LPs_pred, nfr, T=300):
assert Es.shape == dEs.shape
kT = kb * T
mbar = MBAR(Es / kT, nfr)
w = mbar.getWeights()
wt = w.T
dgdl_int = []
for i in range(len(LPs_pred)):
dgdl_int.append(np.dot(wt[i], dEs[i]))
return np.array(dgdl_int)
def test_mbar_getWeights():
""" testing getWeights """
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')
mbar = MBAR(u_kn, N_k)
# rows should be equal to zero
W = mbar.getWeights()
sumrows = np.sum(W, axis=0)
eq(sumrows, np.ones(len(sumrows)), decimal=precision)
def test_mbar_getWeights():
""" testing getWeights """
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')
mbar = MBAR(u_kn, N_k)
# rows should be equal to zero
W = mbar.getWeights()
sumrows = np.sum(W,axis=0)
eq(sumrows, np.ones(len(sumrows)), decimal=precision)
if state_index == len(state_ranges) - 1 and T == state[1]:
state_counts[state_index] = state_counts[state_index] + 1
exit
state_index = state_index + 1
# print("The distribution with "+str(num_states)+" states is: "+str(state_counts))
# Store the distribution data for this number of states so that we can plot comparisons later
for state in range(0, num_states):
distributions_for_each_num_states[num_states_index][
state] = state_counts[state]
state_temps_for_each_num_states[num_states_index][state] = sum(
state_ranges[state]) / 2
T_ranges_for_each_num_states[num_states_index][state] = state_ranges[state]
# Initialize MBAR
mbar = MBAR(u_kn, state_counts, verbose=False, relative_tolerance=1e-12)
# Get the 'weights', or reweighted mixture distribution
weights = mbar.getWeights()
# Store the weights for plot comparisons later on
for sample in range(0, len(weights)):
for state in range(0, num_states):
# print(weights_for_each_num_states[num_states_index][state][sample])
weights_for_each_num_states[num_states_index][state][
sample] = weights[sample][state]
#############
#
# 5) Calculate dimensionless free energies with MBAR
#
#############
# Get the dimensionless free energy differences
free_energies, uncertainty_free_energies = mbar.getFreeEnergyDifferences(
############# # # 5) Calculate 'weights' for each thermodynamic state (temp.) with MBAR # ############# # 'u_kn' contains the decorrelated reduced potential energies evaluated at each temperature (state) u_kn = np.array([[float(float(U_uncorrelated[sample])/float(temperature)) for sample in range(0,len(U_uncorrelated))] for temperature in temperatures]) u_kn_same_total_samples = np.array([[float(float(U_uncorrelated_same_total_samples[sample])/float(temperature)) for sample in range(0,len(U_uncorrelated_same_total_samples))] for temperature in temperatures]) # Initialize MBAR mbar = MBAR(u_kn, state_counts, verbose=False, relative_tolerance=1e-12) # Initialize MBAR using u_kn_same_total_samples, constructed from uniform sampling (same total # of samples) mbar_same_total_samples = MBAR(u_kn_same_total_samples, state_counts_same_total_samples, verbose=False, relative_tolerance=1e-12) # Get the 'weights', or reweighted mixture distribution weights = mbar.getWeights() weights_same_total_samples = mbar_same_total_samples.getWeights() # Store the weights for later analysis weights_for_each_num_states.extend([weights]) weights_for_each_num_states_same_total_samples.extend([weights_same_total_samples]) ############# # # 6) Calculate dimensionless free energies with MBAR # ############# # Get the dimensionless free energy differences, and uncertainties in their values free_energies,uncertainty_free_energies = mbar.getFreeEnergyDifferences()[0],mbar.getFreeEnergyDifferences()[1] # Save the free energies free_energies_for_each_num_states.extend([free_energies])
print "Interfaces"
# Create Interaces around first and last frame of the first trajectory and use only solute coordinates
traj = simulator.storage.trajectory(1)[ [0,-1] ].solute
# mat = tis.compute_N_IJ_l_kI()
# print mat
if simulator.storage.number_of_trajectories() > 100:
u_kln, N_k = tis.compute_mbar_array(0, 0, infinite_energy = 1e5)
mbar = MBAR(u_kln, N_k, method = 'adaptive', relative_tolerance=1.0e-10, verbose=False)
weights = mbar.getWeights()
n = np.zeros(weights.shape, dtype='float')
print np.exp(-mbar.f_k)
print N_k
index = 0
for k,n_samples in enumerate(N_k):
n[index:index+int(n_samples),0:k+1] = 1.0
index += int(n_samples)
print n
print weights[:,0]
print weights[:,2]
