def test_mcmc_algorithm_1_2x2_elber():
"""
Test a simple 2x2 Elber system to ensure that the correct matrices
are generated and sampled. Optionally generate a plot showing this.
"""
max_x = 3.0 * 12.0 / 500.0
max_y = 3.0 * 30.0 / 150.0
N = np.array([[0, 12], [30, 0]])
t = np.array([[500],[150]])
R = np.array([[500], [150]])
model = base.Model()
model.num_milestones = 2
data_sample = common_analyze.Data_sample(model)
data_sample.N_ij = N
data_sample.R_i = t
data_sample.compute_rate_matrix()
Q = data_sample.Q
assert Q[0,0] == -12.0 / 500.0
assert Q[0,1] == 12.0 / 500.0
assert Q[1,0] == 30.0 / 150.0
assert Q[1,1] == -30.0 / 150.0
algorithm = markov_chain_monte_carlo\
.irreversible_stochastic_matrix_algorithm_sample
mcmc_algorithm_any_2x2_common(algorithm, Q, N, R, max_x, max_y,
make_plot=False)
return
def make_simple_model():
n_anchors = 3
n_milestones = 3
# generate data to feed directly into MMVT_data_sample()
model = base.Model()
model.calculation_type = "elber"
model.num_milestones = n_milestones
model.num_anchors = n_anchors
anchor0 = elber_base.Elber_toy_anchor()
anchor0.index = 0
anchor0.endstate = True
milestone_0_0 = base.Milestone()
milestone_0_0.index = 0
milestone_0_0.neighbor_anchor_index = 0
milestone_0_0.alias_index = 2
milestone_0_1 = base.Milestone()
milestone_0_1.index = 1
milestone_0_1.neighbor_anchor_index = 1
milestone_0_1.alias_index = 3
anchor0.milestones = [milestone_0_0, milestone_0_1]
anchor1 = elber_base.Elber_toy_anchor()
anchor1.index = 1
milestone_1_0 = base.Milestone()
milestone_1_0.index = 0
milestone_1_0.neighbor_anchor_index = 0
milestone_1_0.alias_index = 1
milestone_1_1 = base.Milestone()
milestone_1_1.index = 1
milestone_1_1.neighbor_anchor_index = 1
milestone_1_1.alias_index = 2
milestone_1_2 = base.Milestone()
milestone_1_2.index = 2
milestone_1_2.neighbor_anchor_index = 2
milestone_1_2.alias_index = 3
anchor1.milestones = [milestone_1_0, milestone_1_1, milestone_1_2]
anchor2 = elber_base.Elber_toy_anchor()
anchor2.index = 2
milestone_2_0 = base.Milestone()
milestone_2_0.index = 1
milestone_2_0.neighbor_anchor_index = 1
milestone_2_0.alias_index = 1
milestone_2_1 = base.Milestone()
milestone_2_1.index = 2
milestone_2_1.neighbor_anchor_index = 2
milestone_2_1.alias_index = 2
anchor2.milestones = [milestone_2_0, milestone_2_1]
anchor2.bulkstate = True
model.anchors = [anchor0, anchor1, anchor2]
return model
def make_simple_model():
n_anchors = 4
n_milestones = 3
# generate data to feed directly into MMVT_data_sample()
model = base.Model()
model.num_milestones = n_milestones
model.num_anchors = n_anchors
anchor0 = mmvt_base.MMVT_toy_anchor()
anchor0.index = 0
milestone_0_0 = base.Milestone()
milestone_0_0.index = 0
milestone_0_0.neighbor_anchor_index = 1
milestone_0_0.alias_index = 1
anchor0.milestones = [milestone_0_0]
anchor1 = mmvt_base.MMVT_toy_anchor()
anchor1.index = 1
milestone_1_0 = base.Milestone()
milestone_1_0.index = 0
milestone_1_0.neighbor_anchor_index = 0
milestone_1_0.alias_index = 1
milestone_1_1 = base.Milestone()
milestone_1_1.index = 1
milestone_1_1.neighbor_anchor_index = 2
milestone_1_1.alias_index = 2
anchor1.milestones = [milestone_1_0, milestone_1_1]
anchor2 = mmvt_base.MMVT_toy_anchor()
anchor2.index = 2
milestone_2_0 = base.Milestone()
milestone_2_0.index = 1
milestone_2_0.neighbor_anchor_index = 1
milestone_2_0.alias_index = 1
milestone_2_1 = base.Milestone()
milestone_2_1.index = 2
milestone_2_1.neighbor_anchor_index = 3
milestone_2_1.alias_index = 2
anchor2.milestones = [milestone_2_0, milestone_2_1]
anchor3 = mmvt_base.MMVT_toy_anchor()
anchor3.index = 3
milestone_3_0 = base.Milestone()
milestone_3_0.index = 2
milestone_3_0.neighbor_anchor_index = 2
milestone_3_0.alias_index = 1
anchor3.milestones = [milestone_3_0]
model.anchors = [anchor0, anchor1, anchor2, anchor3]
return model
def generate_seekr2_model_and_filetree(model_input, force_overwrite):
"""
Using the Model_input from the user, prepare the Model
object and the filetree. Then prepare all building files
for each anchor and serialize the Model to XML.
"""
model = common_prepare.model_factory(model_input)
common_prepare.prepare_model_cvs_and_anchors(model, model_input)
root_directory = os.path.expanduser(model_input.root_directory)
xml_path = os.path.join(root_directory, "model.xml")
if os.path.exists(xml_path):
# then a model file already exists at this location: update
# the anchor directories.
old_model = base.Model()
old_model.deserialize(xml_path)
common_prepare.modify_model(old_model, model, root_directory,
force_overwrite)
filetree.generate_filetree(model, root_directory)
filetree.copy_building_files(model, model_input, root_directory)
common_prepare.generate_bd_files(model, root_directory)
model.serialize(xml_path)
return model, xml_path
cuda_device_index = args["cuda_device_index"]
min_total_simulation_length = args["min_total_simulation_length"]
max_total_simulation_length = args["max_total_simulation_length"]
convergence_cutoff = args["convergence_cutoff"]
minimum_anchor_transitions = args["minimum_anchor_transitions"]
directory = args["directory"]
force_overwrite = args["force_overwrite"]
save_state_file = args["save_state_file"]
namd_command = args["namd_command"]
namd_arguments = args["namd_arguments"]
minimum_b_surface_trajectories = args["minimum_b_surface_trajectories"]
minimum_bd_milestone_trajectories = args[
"minimum_bd_milestone_trajectories"]
max_b_surface_trajs_to_extract = args["max_b_surface_trajs_to_extract"]
min_b_surface_encounters = args["min_b_surface_encounters"]
min_bd_milestone_encounters = args["min_bd_milestone_encounters"]
num_rev_launches = args["num_rev_launches"]
umbrella_restart_mode = args["umbrella_restart_mode"]
assert os.path.exists(input_file), "A nonexistent input file was provided."
model = base.Model()
model.deserialize(input_file)
run(model, instruction, min_total_simulation_length,
max_total_simulation_length, convergence_cutoff, directory,
minimum_anchor_transitions, cuda_device_index, force_overwrite,
save_state_file, namd_command, namd_arguments,
minimum_b_surface_trajectories, minimum_bd_milestone_trajectories,
max_b_surface_trajs_to_extract, min_b_surface_encounters,
min_bd_milestone_encounters, num_rev_launches, umbrella_restart_mode)
def make_test_model(tmp_dir,
num_anchors=3,
milestone_type="spherical",
make_dir=True,
mode="amber",
engine="openmm",
no_BD=False):
if milestone_type == "spherical":
#group1 = [2478, 2489, 2499, 2535, 2718, 2745, 2769, 2787, 2794, 2867,
# 2926]
group1 = list(range(147))
#group2 = [3221, 3222, 3223, 3224, 3225, 3226, 3227, 3228, 3229]
group2 = list(range(147, 162))
groups = [group1, group2]
cv1 = mmvt_base.MMVT_spherical_CV(index=0, groups=groups)
cvs = [cv1]
else:
raise Exception("milestone type not implemented")
mymodel = base.Model()
mymodel.temperature = 277.8
mymodel.calculation_type = "MMVT"
mymodel.calculation_settings = mmvt_base.MMVT_settings()
mymodel.calculation_settings.num_production_steps = 100
mymodel.anchor_rootdir = tmp_dir
mymodel.num_anchors = num_anchors
mymodel.num_milestones = num_anchors - 1
if engine == "openmm":
mymodel.openmm_settings = base.Openmm_settings()
mymodel.openmm_settings.energy_reporter_frequency = 50
mymodel.openmm_settings.trajectory_reporter_frequency = 50
mymodel.openmm_settings.restart_checkpoint_frequency = 50
mymodel.openmm_settings.total_simulation_length = 50
mymodel.openmm_settings.cuda_platform_settings = None
mymodel.openmm_settings.reference_platform = True
if engine == "namd":
mymodel.namd_settings = base.Namd_settings()
mymodel.namd_settings.energy_reporter_frequency = 50
mymodel.namd_settings.trajectory_reporter_frequency = 50
mymodel.namd_settings.restart_checkpoint_frequency = 50
mymodel.namd_settings.total_simulation_length = 50
if not no_BD:
my_k_on_info = base.K_on_info()
my_k_on_info.source_milestones = [2]
mymodel.k_on_info = my_k_on_info
mymodel.collective_variables = cvs
'''
for index in range(num_anchors):
milestone_list = []
if index < num_anchors-1:
milestone1 = base.Milestone()
milestone1.index = index
milestone1.neighbor_index = index+1
milestone1.alias_id = 2
milestone1.cv_index = 0
milestone1.variables = {"radius": 1.3}
milestone_list.append(milestone1)
bulkstate = False
else:
bulkstate = True
if index > 0:
milestone2 = base.Milestone()
milestone2.index = index-1
milestone2.neighbor_index = index-1
milestone2.alias_id = 1
milestone2.cv_index = 0
milestone2.variables = {"radius": 1.1}
milestone_list.append(milestone2)
endstate = False
else:
endstate = True
if bulkstate:
endstate = True
'''
for i in range(num_anchors):
index = i
milestone_list = []
num_milestones = 0
if i > 0:
milestone1 = base.Milestone()
milestone1.index = i - 1
milestone1.neighbor_anchor_index = i - 1
milestone1.alias_index = num_milestones + 1
milestone1.cv_index = 0
milestone1.variables = {"k": -1.0, "radius": 0.1 * i}
milestone_list.append(milestone1)
num_milestones += 1
endstate = False
else:
endstate = True
if i < num_anchors - 1:
milestone2 = base.Milestone()
milestone2.index = i
milestone2.neighbor_anchor_index = i + 1
milestone2.alias_index = num_milestones + 1
milestone2.cv_index = 0
milestone2.variables = {"k": 1.0, "radius": 0.1 * (i + 1)}
milestone_list.append(milestone2)
bulkstate = False
else:
bulkstate = True
name = "anchor_%d" % index
directory = name
if make_dir:
full_anchor_dir = os.path.join(tmp_dir, directory)
os.mkdir(full_anchor_dir)
prod_dir = os.path.join(full_anchor_dir, "prod")
os.mkdir(prod_dir)
building_dir = os.path.join(full_anchor_dir, "building")
os.mkdir(building_dir)
md_glob = "mmvt*.out"
md = True
bd = False
#anchor = base.Anchor(name, index, directory, md_glob, md, bd,
# endstate, bulkstate, len(milestone_list), milestone_list)
anchor = mmvt_base.MMVT_anchor()
anchor.name = name
anchor.index = index
anchor.directory = directory
anchor.md_mmvt_output_glob = md_glob
anchor.md = md
anchor.bd = bd
anchor.endstate = endstate
anchor.bulkstate = bulkstate
#anchor.num_milestones = len(milestone_list)
anchor.milestones = milestone_list
if mode == 'amber':
make_amber_params(anchor, building_dir, engine=engine)
elif mode == 'forcefield':
make_forcefield_params(anchor, building_dir)
else:
raise Exception("mode not implemented:" + mode)
mymodel.anchors.append(anchor)
if not no_BD:
mymodel.browndye_settings = base.Browndye_settings()
mymodel.browndye_settings.apbs_grid_spacing = 0.5
mymodel.browndye_settings.n_threads = 1
mymodel.browndye_settings.recompute_ligand_electrostatics = 1
mymodel.browndye_settings.debye_length = 7.5612
mymodel.browndye_settings.ghost_indices_rec = [147]
mymodel.browndye_settings.ghost_indices_lig = [15]
mymodel.k_on_info.b_surface_directory = "b_surface"
mymodel.k_on_info.b_surface_num_steps = 100
ion1 = base.Ion()
ion1.radius = 1.2
ion1.charge = -1.0
ion1.conc = 0.15
ion2 = base.Ion()
ion2.radius = 0.9
ion2.charge = 1.0
ion2.conc = 0.15
mymodel.k_on_info.ions = [ion1, ion2]
if make_dir:
b_surface_abs_path = os.path.join(tmp_dir, "b_surface")
os.mkdir(b_surface_abs_path)
make_browndye_params_b_surface(mymodel, b_surface_abs_path)
mymodel.k_on_info.bd_milestones = []
bd_milestone = base.BD_milestone()
bd_milestone.index = 0
bd_milestone.name = "bd_milestone_0"
bd_milestone.directory = bd_milestone.name
bd_milestone.outer_milestone = milestone2
#bd_milestone.outer_milestone = mymodel.anchors[-1].milestones[-1]
assert "radius" in bd_milestone.outer_milestone.variables, \
"A BD outer milestone must be spherical."
bd_milestone.inner_milestone = milestone1
bd_milestone.num_steps = 1
bd_milestone.receptor_indices = list(range(147))
bd_milestone.ligand_indices = list(range(15))
mymodel.k_on_info.bd_milestones.append(bd_milestone)
for bd_milestone in mymodel.k_on_info.bd_milestones:
bd_milestone_dict = {}
bd_milestone_dict[bd_milestone.extracted_directory] = {}
bd_milestone_dict[bd_milestone.fhpd_directory] = {}
bd_milestone_filetree = filetree.Filetree(
{bd_milestone.directory: bd_milestone_dict})
bd_milestone_filetree.make_tree(tmp_dir)
return mymodel
def make_smol_model(tmp_path, num_anchors, intervals):
basename = "testmmvt.dat"
mymodel = base.Model()
mymodel.temperature = 300.0
mymodel.calculation_type = "mmvt"
mymodel.anchor_rootdir = tmp_path
mymodel.num_anchors = num_anchors+1
mymodel.num_milestones = mymodel.num_anchors - 1
# TEMPORARY: toy system will eventually have its own settings
mymodel.openmm_settings = base.Openmm_settings()
intervals.append(1.0)
for index in range(mymodel.num_anchors):
boundary1 = float(index)
boundary2 = float(index)+intervals[index]
x0 = boundary1 + 0.5
milestones = []
anchor = mmvt_base.MMVT_anchor()
if index == 0:
milestone_count = 1
milestone1 = base.Milestone()
milestone1.index = 0
milestone1.neighbor_anchor_index = 1
milestone1.alias_index = 1
milestone1.cv_index = 0
milestones.append(milestone1)
anchor.bulkstate = False
end_state = True
elif index > 0 and index < mymodel.num_anchors-1:
milestone_count = 2
milestone1 = base.Milestone()
milestone1.index = index-1
milestone1.neighbor_anchor_index = index-1
milestone1.alias_index = 1
milestone1.cv_index = 0
milestone2 = base.Milestone()
milestone2.index = index
milestone2.neighbor_anchor_index = index+1
milestone2.alias_index = 2
milestone2.cv_index = 0
milestones.append(milestone1)
milestones.append(milestone2)
anchor.bulkstate = False
end_state = False
elif index == mymodel.num_anchors-1:
milestone_count = 1
milestone1 = base.Milestone()
milestone1.index = index-1
milestone1.neighbor_anchor_index = index-1
milestone1.alias_index = 1
milestone1.cv_index = 0
milestones.append(milestone1)
anchor.bulkstate = True
end_state = False
anchor.name = "anchor_%d" % index
anchor.index = index
anchor.directory = anchor.name
anchor.md_mmvt_output_glob = basename
anchor.md = True
anchor.endstate = end_state
anchor.milestones = milestones
mymodel.anchors.append(anchor)
return mymodel
def model_factory(model_input, use_absolute_directory=False):
"""
Given the Model_input object, which contains the settings,
create the Model object which contains more detailed
information.
"""
model = base.Model()
calc_settings = model_input.calculation_settings
if model_input.calculation_type.lower() == "mmvt":
model.calculation_settings = mmvt_base.MMVT_settings()
model.calculation_settings.num_production_steps = \
calc_settings.md_steps_per_anchor
model.calculation_settings.energy_reporter_interval = \
calc_settings.md_output_interval
model.calculation_settings.restart_checkpoint_interval = \
calc_settings.md_output_interval
model.calculation_settings.trajectory_reporter_interval = \
calc_settings.md_output_interval
elif model_input.calculation_type.lower() == "elber":
model.calculation_settings = elber_base.Elber_settings()
model.calculation_settings.temperature_equil_progression = \
calc_settings.temperature_equil_progression
model.calculation_settings.num_temperature_equil_steps = \
calc_settings.num_temperature_equil_steps
model.calculation_settings.num_umbrella_stage_steps = \
calc_settings.num_umbrella_stage_steps
model.calculation_settings.umbrella_force_constant = \
calc_settings.umbrella_force_constant
model.calculation_settings.fwd_rev_interval = \
calc_settings.fwd_rev_interval
model.calculation_settings.umbrella_energy_reporter_interval = \
calc_settings.fwd_rev_interval
model.calculation_settings.umbrella_trajectory_reporter_interval = \
calc_settings.fwd_rev_interval
model.calculation_settings.rev_energy_reporter_interval = \
calc_settings.rev_output_interval
model.calculation_settings.rev_trajectory_reporter_interval = \
calc_settings.rev_output_interval
model.calculation_settings.fwd_energy_reporter_interval = \
calc_settings.fwd_output_interval
model.calculation_settings.fwd_trajectory_reporter_interval = \
calc_settings.fwd_output_interval
else:
raise Exception("Invalid calculation_type entered:",
model_input.calculation_type)
model.calculation_type = model_input.calculation_type.lower()
temperature = model_input.temperature
model.temperature = temperature
if use_absolute_directory:
model.anchor_rootdir = model_input.root_directory
else:
model.anchor_rootdir = "."
if model_input.ensemble.lower() == "nvt":
pressure = None
elif model_input.ensemble.lower() == "npt":
pressure = model_input.pressure
elif model_input.ensemble.lower() == "nve":
raise Exception("NVE ensemble not yet implemented. Options must be"\
"NVT or NPT.")
else:
raise Exception("Invalid ensemble entered:", model_input.ensemble)
if model_input.md_program.lower() == "openmm":
mm_settings = base.Openmm_settings()
mm_settings.langevin_integrator.target_temperature = temperature
if pressure is not None:
mm_settings.barostat = base.Barostat_settings_openmm()
mm_settings.barostat.target_temperature = temperature
mm_settings.barostat.target_pressure = pressure
mm_settings.initial_temperature = temperature
mm_settings.nonbonded_cutoff = model_input.nonbonded_cutoff
mm_settings.run_minimization = model_input.run_minimization
mm_settings.hydrogenMass = model_input.hydrogenMass
mm_settings.constraints = model_input.constraints
mm_settings.langevin_integrator.timestep = model_input.timestep
mm_settings.rigidWater = model_input.rigidWater
model.openmm_settings = mm_settings
elif model_input.md_program.lower() == "namd":
namd_settings = base.Namd_settings()
namd_settings.langevin_integrator.target_temperature = temperature
if pressure is not None:
namd_settings.barostat = base.Barostat_settings_namd()
namd_settings.barostat.target_temperature = temperature
namd_settings.barostat.target_pressure = pressure
namd_settings.initial_temperature = temperature
namd_settings.nonbonded_cutoff = model_input.nonbonded_cutoff
namd_settings.run_minimization = model_input.run_minimization
assert model_input.hydrogenMass is None, "hydrogen mass "\
"repartitioning not yet implemented in NAMD SEEKR."
#namd_settings.hydrogenMass = model_input.hydrogenMass
namd_settings.constraints = model_input.constraints
namd_settings.langevin_integrator.timestep = model_input.timestep
namd_settings.rigidWater = model_input.rigidWater
model.namd_settings = namd_settings
else:
raise Exception("Invalid MD program entered:", model_input.md_program)
if model_input.browndye_settings_input is None:
# Running no BD
pass
else:
k_on_info = base.K_on_info()
k_on_info.ions = model_input.browndye_settings_input.ions
k_on_info.b_surface_num_trajectories = \
model_input.browndye_settings_input.num_b_surface_trajectories
model.browndye_settings = base.Browndye_settings()
model.browndye_settings.browndye_bin_dir = \
model_input.browndye_settings_input.binary_directory
model.browndye_settings.receptor_pqr_filename = \
os.path.basename(
model_input.browndye_settings_input.receptor_pqr_filename)
model.browndye_settings.ligand_pqr_filename = \
os.path.basename(
model_input.browndye_settings_input.ligand_pqr_filename)
model.browndye_settings.apbs_grid_spacing = \
model_input.browndye_settings_input.apbs_grid_spacing
model.browndye_settings.n_threads = \
model_input.browndye_settings_input.n_threads
model.k_on_info = k_on_info
return model
def test_mcmc_3x3_mmvt(tmpdir_factory):
"""
Use the same statistics to generate both MMVT and Elber rate matrices.
Then use the data to sample matrices and compare the distributions to
ensure that the MMVT and Elber matrix sampling methods are consistent.
"""
num = 10000 #100000
stride = 4
skip = 90
n_anchors = 4
n_milestones = 3
# generate data to feed directly into MMVT_data_sample()
model = base.Model()
model.num_milestones = n_milestones
model.num_anchors = n_anchors
anchor0 = mmvt_base.MMVT_toy_anchor()
anchor0.index = 0
milestone_0_0 = base.Milestone()
milestone_0_0.index = 0
milestone_0_0.neighbor_anchor_index = 1
milestone_0_0.alias_index = 1
anchor0.milestones = [milestone_0_0]
anchor1 = mmvt_base.MMVT_toy_anchor()
anchor1.index = 1
milestone_1_0 = base.Milestone()
milestone_1_0.index = 0
milestone_1_0.neighbor_anchor_index = 0
milestone_1_0.alias_index = 1
milestone_1_1 = base.Milestone()
milestone_1_1.index = 1
milestone_1_1.neighbor_anchor_index = 2
milestone_1_1.alias_index = 2
anchor1.milestones = [milestone_1_0, milestone_1_1]
anchor2 = mmvt_base.MMVT_toy_anchor()
anchor2.index = 2
milestone_2_0 = base.Milestone()
milestone_2_0.index = 1
milestone_2_0.neighbor_anchor_index = 1
milestone_2_0.alias_index = 1
milestone_2_1 = base.Milestone()
milestone_2_1.index = 2
milestone_2_1.neighbor_anchor_index = 3
milestone_2_1.alias_index = 2
anchor2.milestones = [milestone_2_0, milestone_2_1]
anchor3 = mmvt_base.MMVT_toy_anchor()
anchor3.index = 3
milestone_3_0 = base.Milestone()
milestone_3_0.index = 2
milestone_3_0.neighbor_anchor_index = 2
milestone_3_0.alias_index = 1
anchor3.milestones = [milestone_3_0]
model.anchors = [anchor0, anchor1, anchor2, anchor3]
# MMVT stats
N_alpha_beta = {(0,1):12, (1,0):12,
(1,2):12, (2,1):12,
(2,3):6, (3,2):6}
k_alpha_beta = {(0,1):20.0, (1,0):10.0,
(1,2):10.0, (2,1):(40.0/3.0),
(2,3):(20.0/3.0), (3,2):20.0}
N_i_j_alpha = [{},
{(0,1):4, (1,0):4},
{(1,2):2, (2,1):2},
{}]
R_i_alpha_total = [{0: 1.2},
{0: 1.2, 1:1.2},
{1: 1.2, 2:0.6},
{2: 0.6}]
T_alpha_total = [1.2,
2.4,
1.8,
0.6]
main_data_sample = mmvt_analyze.MMVT_data_sample(
model, N_alpha_beta, k_alpha_beta, N_i_j_alpha,
R_i_alpha_total, T_alpha_total)
main_data_sample.calculate_pi_alpha()
main_data_sample.fill_out_data_quantities()
main_data_sample.compute_rate_matrix()
main_data_sample.calculate_thermodynamics()
main_data_sample.calculate_kinetics()
mmvt_Q = main_data_sample.Q
# Elber stats
N_i_j = {(0,1): 4, (1,0): 4, (1,2): 2, (2,1): 2}
R_i = {0: 2.4, 1: 2.4, 2: 1.2}
elber_N = np.array([[0, 4, 0],
[4, 0, 2],
[0, 2, 0]])
elber_R = np.array([[2.4],
[2.4],
[1.2]])
elber_Q = np.zeros((n_milestones, n_milestones))
for i in range(n_milestones):
for j in range(n_milestones):
key = (i,j)
if key in N_i_j:
elber_Q[i,j] = N_i_j[key] / R_i[i]
for i in range(n_milestones):
elber_Q[i,i] = -np.sum(elber_Q[i,:])
# Make sure the two models make identical matrices
assert np.isclose(mmvt_Q, elber_Q).all()
# Now compare the distributions of both of them
# MMVT matrix sampler
data_sample_list, p_i_error, free_energy_profile_err, MFPTs_error, \
k_off_error, k_ons_error = mmvt_analyze.monte_carlo_milestoning_error(
main_data_sample, num=num, stride=stride, skip=skip, verbose=True)
mmvt_q1_distribution = []
mmvt_q2_distribution = []
for data_sample in data_sample_list:
mmvt_q1_distribution.append(data_sample.Q[0,1])
mmvt_q2_distribution.append(data_sample.Q[1,0])
# Elber matrix sampler
elber_q1_distribution = []
elber_q2_distribution = []
for counter in range(num * (stride) + skip):
#if verbose: print("MCMC stepnum: ", counter)
elber_Qnew = markov_chain_monte_carlo\
.irreversible_stochastic_matrix_algorithm_sample(
elber_Q, elber_N, elber_R)
if counter > skip and counter % stride == 0:
elber_q1_distribution.append(elber_Q[0,1])
elber_q2_distribution.append(elber_Q[1,0])
elber_Q = elber_Qnew
assert np.isclose(np.mean(elber_q1_distribution),
np.mean(mmvt_q1_distribution), rtol=0.5, atol=0.01)
assert np.isclose(np.std(elber_q1_distribution),
np.std(mmvt_q1_distribution), rtol=0.5, atol=0.01)
assert np.isclose(np.mean(elber_q2_distribution),
np.mean(mmvt_q2_distribution), rtol=0.5, atol=0.01)
assert np.isclose(np.std(elber_q2_distribution),
np.std(mmvt_q2_distribution), rtol=0.5, atol=0.01)
return