def test_MMVT_data_sample_fill_out_data_quantities():
model = make_simple_model()
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()
N_ij = {(0,1): 1, (1,0): 1, (1,2): 0.5, (2,1): 0.5}
R_i = {0: 0.6, 1: 0.6, 2: 0.3}
compare_dicts(N_ij, main_data_sample.N_ij)
compare_dicts(R_i, main_data_sample.R_i)
return
def test_Elber_data_sample_fill_out_data_quantities():
model = make_simple_model()
N_i_j_list = [{(0,1): 4}, {(1,0): 4, (1,2): 2}, {(2,1): 2}]
R_i_list = [2.4, 2.4]
N_i_j_exp = {(0,1): 4, (1,0): 4, (1,2): 2}
data_sample = elber_analyze.Elber_data_sample(model, N_i_j_list, R_i_list)
data_sample.fill_out_data_quantities()
compare_dicts(N_i_j_exp, data_sample.N_ij)
assert len(R_i_list) == len(data_sample.R_i)
for item1, item2 in zip(R_i_list, data_sample.R_i):
assert item1 == data_sample.R_i[item2]
return
def test_browndye_run_compute_rate_constant():
"""
Test the function which extracts Browndye results from the
b-surface simulations.
"""
test_bd_results_filename = os.path.join(TEST_DIRECTORY,
"data/sample_bd_results_file.xml")
k_ons, k_on_errors, reaction_probabilities, \
reaction_probability_errors, transition_counts \
= common_analyze.browndye_run_compute_rate_constant(
"compute_rate_constant", [test_bd_results_filename],
sample_error_from_normal=False)
expected_k_ons = {
10: 1.18338e+09,
"escaped": 2.480783458e+10,
"stuck": 0,
"total": 2.599121458e+10
}
compare_dicts(k_ons, expected_k_ons)
expected_k_on_errors = {
10: 1.7539737e+07,
"escaped": 8.0298973e+07,
"stuck": 1e+99,
"total": 8.21918481e+7
}
compare_dicts(k_on_errors, expected_k_on_errors)
expected_reaction_probabilities = {
10: (4553 / 100000),
"escaped": (95447 / 100000),
"stuck": 0.0,
"total": 1.0
}
compare_dicts(reaction_probabilities, expected_reaction_probabilities)
expected_reaction_probability_errors = {
10: 0.0006748333290290023,
"escaped": 0.0030894659734406714,
"stuck": 1e+99,
"total": 0.0031622934716752666
}
compare_dicts(reaction_probability_errors,
expected_reaction_probability_errors)
expected_transition_counts = {
10: 4553,
"escaped": 95447,
"stuck": 0,
"total": 100000
}
compare_dicts(transition_counts, expected_transition_counts)
return
def test_MMVT_anchor_statistics_read_output_file_list(toy_mmvt_model):
"""
Test the read_output_file_list method of the MMVT_anchor_statistics
object.
"""
stats = mmvt_analyze.MMVT_anchor_statistics(1)
engine = "openmm"
output_file_list = [test_output_filename]
min_time = None
max_time = None
anchor = toy_mmvt_model.anchors[1]
timestep = toy_mmvt_model.get_timestep()
stats.read_output_file_list(engine, output_file_list, min_time,
max_time, anchor, timestep)
N_i_j_alpha_dict2 = {(1, 2): 52, (2, 1): 52}
R_i_alpha_dict2 = {1: 1658.696, 2: 198.912}
N_alpha_beta_dict2 = {1: 2423, 2: 98}
T_alpha2 = 1954.760
k_alpha_beta_dict2 = {1: 2423/1954.760, 2: 98/1954.760}
compare_dicts(N_i_j_alpha_dict2, stats.N_i_j_alpha)
compare_dicts(R_i_alpha_dict2, stats.R_i_alpha_total)
compare_dicts(N_alpha_beta_dict2, stats.N_alpha_beta)
assert np.isclose(T_alpha2, stats.T_alpha_total)
compare_dicts(k_alpha_beta_dict2, stats.k_alpha_beta)
stats.print_stats()
return
def test_MMVT_data_sample_fill_k_from_matrices():
model = make_simple_model()
k_alpha_beta_exp = {(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}
k_alpha_beta_matrix = np.array([
[-20.0, 20.0, 0.0, 0.0],
[10.0, -20.0, 10.0, 0.0],
[0.0, (40.0/3.0), -(40.0/3.0)-(20.0/3.0), (20.0/3.0)],
[0.0, 0.0, 20.0, -20.0]])
main_data_sample = mmvt_analyze.MMVT_data_sample(
model, None, None, None,
None, None)
main_data_sample.fill_k_from_matrices(k_alpha_beta_matrix)
compare_dicts(k_alpha_beta_exp, main_data_sample.k_alpha_beta)
return
def test_Analysis_fill_out_data_samples_elber():
model = test_elber_analyze.make_simple_model()
N_i_j_list = [{3: 4}, {1: 4, 3: 2}, {1: 2}]
R_i_list = [2.4, 2.4, 1.2]
N_i_j_list_exp = [{(0,1): 4}, {(1,0): 4, (1,2): 2}]
R_i_list_exp = [2.4, 2.4]
analysis = analyze.Analysis(model)
for alpha in range(model.num_anchors):
anchor_stats = elber_analyze.Elber_anchor_statistics(alpha)
anchor_stats.N_i_j = N_i_j_list[alpha]
anchor_stats.R_i_total = R_i_list[alpha]
analysis.anchor_stats_list.append(anchor_stats)
analysis.fill_out_data_samples_elber()
for alpha in range(model.num_anchors):
if model.anchors[alpha].bulkstate:
continue
compare_dicts(N_i_j_list_exp[alpha],
analysis.main_data_sample.N_i_j_list[alpha])
assert analysis.main_data_sample.R_i_list[alpha] == R_i_list_exp[alpha]
return
def test_analyze_bd_only(host_guest_mmvt_model):
"""
Test function analyze_bd_only(). This one also tests
get_bd_transition_counts().
"""
test_bd_results_filename = os.path.join(
TEST_DIRECTORY, "data/sample_bd_results_file2.xml")
b_surface_directory = os.path.join(host_guest_mmvt_model.anchor_rootdir,
"b_surface")
b_surface_results_file = os.path.join(b_surface_directory, "results1.xml")
data_sample = common_analyze.Data_sample(host_guest_mmvt_model)
copyfile(test_bd_results_filename, b_surface_results_file)
common_converge.analyze_bd_only(host_guest_mmvt_model, data_sample)
assert len(data_sample.bd_transition_counts) == 1
expected_counts_b_surface = {
"total": 100000,
"escaped": 54070,
12: 65239,
11: 45930,
"stuck": 0
}
counts_b_surface = data_sample.bd_transition_counts["b_surface"]
compare_dicts(counts_b_surface, expected_counts_b_surface)
return
def test_Data_sample_parse_browndye_results(host_guest_mmvt_model):
test_bd_results_filename = os.path.join(
TEST_DIRECTORY, "data/sample_bd_results_file2.xml")
b_surface_directory = os.path.join(host_guest_mmvt_model.anchor_rootdir,
"b_surface")
b_surface_results_file = os.path.join(b_surface_directory, "results1.xml")
data_sample = common_analyze.Data_sample(host_guest_mmvt_model)
copyfile(test_bd_results_filename, b_surface_results_file)
data_sample.parse_browndye_results()
assert len(data_sample.bd_transition_counts) == 2
expected_counts_b_surface = {
"total": 100000,
"escaped": 54070,
12: 65239,
11: 45930,
"stuck": 0
}
counts_b_surface = data_sample.bd_transition_counts["b_surface"]
compare_dicts(counts_b_surface, expected_counts_b_surface)
expected_counts_bd_milestone0 = {
"total": 65239,
"escaped": 65239 - 45930,
11: 45930
}
counts_bd_milestone0 = data_sample.bd_transition_counts[0]
compare_dicts(counts_bd_milestone0, expected_counts_bd_milestone0)
expected_b_surface_probabilities = {
"total": 1.0,
"escaped": 54070 / 100000,
12: 65239 / 100000,
11: 45930 / 100000,
"stuck": 0.0
}
b_surface_probabilities \
= data_sample.bd_transition_probabilities["b_surface"]
compare_dicts(b_surface_probabilities, expected_b_surface_probabilities)
expected_probabilities_bd_milestone0 = {
"total": 1.0,
"escaped": (65239 - 45930) / 65239,
11: 45930 / 65239
}
probabilities_bd_milestone0 \
= data_sample.bd_transition_probabilities[0]
compare_dicts(probabilities_bd_milestone0,
expected_probabilities_bd_milestone0)
k_on = data_sample.b_surface_k_ons_src["total"]
expected_k_ons = {
"total": k_on,
"escaped": k_on * 54070 / 100000,
"stuck": 0.0,
12: k_on * 65239 / 100000,
11: k_on * 45930 / 100000
}
k_ons = data_sample.b_surface_k_ons_src
compare_dicts(expected_k_ons, k_ons)
k_on_err = data_sample.b_surface_b_surface_k_on_errors_src
expected_k_ons_err = {
"total": k_on / np.sqrt(100000 - 1),
"escaped": k_on * (54070 / 100000) / np.sqrt(54070 - 1),
"stuck": 1e99,
12: k_on * (65239 / 100000) / np.sqrt(65239 - 1),
11: k_on * (45930 / 100000) / np.sqrt(45930 - 1)
}
compare_dicts(k_on_err, expected_k_ons_err)
return
def test_Analysis_fill_out_data_samples_mmvt():
model = test_mmvt_analyze.make_simple_model()
N_alpha_beta = [{1:12},
{1:12, 2:12},
{1:12, 2:6},
{1:6}]
k_alpha_beta = [{1:20.0},
{1:10.0, 2:10.0},
{1:(40.0/3.0), 2:(20.0/3.0)},
{1:20.0}]
N_i_j_alpha_list = [{},
{(1,2):4, (2,1):4},
{(1,2):2, (2,1):2},
{}]
R_i_alpha_total_list = [{1: 1.2},
{1: 1.2, 2:1.2},
{1: 1.2, 2:0.6},
{1: 0.6}]
T_alpha_total = [1.2,
2.4,
1.8,
0.6]
analysis = analyze.Analysis(model)
for alpha in range(model.num_anchors):
anchor_stats = mmvt_analyze.MMVT_anchor_statistics(alpha)
anchor_stats.N_i_j_alpha = N_i_j_alpha_list[alpha]
anchor_stats.R_i_alpha_total = R_i_alpha_total_list[alpha]
anchor_stats.T_alpha_total = T_alpha_total[alpha]
anchor_stats.N_alpha_beta = N_alpha_beta[alpha]
anchor_stats.k_alpha_beta = k_alpha_beta[alpha]
analysis.anchor_stats_list.append(anchor_stats)
analysis.fill_out_data_samples_mmvt()
N_alpha_beta_exp = {(0,1):12, (1,0):12,
(1,2):12, (2,1):12,
(2,3):6, (3,2):6}
k_alpha_beta_exp = {(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_exp = [{},
{(0,1):4, (1,0):4},
{(1,2):2, (2,1):2},
{}]
R_i_alpha_total_exp = [{0: 1.2},
{0: 1.2, 1:1.2},
{1: 1.2, 2:0.6},
{2: 0.6}]
T_alpha_total_exp = [1.2,
2.4,
1.8,
0.6]
compare_dicts(analysis.main_data_sample.N_alpha_beta, N_alpha_beta_exp)
compare_dicts(analysis.main_data_sample.k_alpha_beta, k_alpha_beta_exp)
for dict1, dict2 in zip(analysis.main_data_sample.N_i_j_alpha,
N_i_j_alpha_exp):
compare_dicts(dict1, dict2)
for dict1, dict2 in zip(analysis.main_data_sample.R_i_alpha,
R_i_alpha_total_exp):
compare_dicts(dict1, dict2)
for val1, val2 in zip(analysis.main_data_sample.T_alpha,
T_alpha_total_exp):
assert np.isclose(val1, val2)
return
def test_MMVT_data_sample_fill_N_R_alpha_from_matrices():
model = make_simple_model()
N_i_j_alpha_exp = [{},
{(0,1):4, (1,0):4},
{(1,2):2, (2,1):2},
{}]
R_i_alpha_total_exp = [{0: 1.2},
{0: 1.2, 1:1.2},
{1: 1.2, 2:0.6},
{2: 0.6}]
mmvt_Nij_alpha = [
np.array([
[0, 0, 0],
[0, 0, 0],
[0, 0, 0]]),
np.array([
[0, 4, 0],
[4, 0, 0],
[0, 0, 0]]),
np.array([
[0, 0, 0],
[0, 0, 2],
[0, 2, 0]]),
np.array([
[0, 0, 0],
[0, 0, 0],
[0, 0, 0]])]
mmvt_Ri_alpha = [
np.array([
[1.2],
[0],
[0]]),
np.array([
[1.2],
[1.2],
[0]]),
np.array([
[0],
[1.2],
[0.6]]),
np.array([
[0],
[0],
[0.6]])]
N_i_j_alpha_start = [{},
{(0,1):3, (1,0):3},
{(1,2):3, (2,1):3},
{}]
R_i_alpha_total_start = [{0: 1.0},
{0: 1.0, 1:1.0},
{1: 1.0, 2:0.4},
{2: 0.4}]
T_alpha_total_start = [1.2,
2.4,
1.8,
0.6]
main_data_sample = mmvt_analyze.MMVT_data_sample(
model, None, None, N_i_j_alpha_start,
R_i_alpha_total_start, T_alpha_total_start)
main_data_sample.fill_N_R_alpha_from_matrices(mmvt_Nij_alpha, mmvt_Ri_alpha)
assert len(N_i_j_alpha_exp) == len(main_data_sample.N_i_j_alpha)
for alpha in range(len(N_i_j_alpha_exp)):
mmvt_Nij_exp = N_i_j_alpha_exp[alpha]
mmvt_Nij = main_data_sample.N_i_j_alpha[alpha]
compare_dicts(mmvt_Nij_exp, mmvt_Nij)
assert len(R_i_alpha_total_exp) == len(main_data_sample.R_i_alpha)
for alpha in range(len(R_i_alpha_total_exp)):
mmvt_Ri_exp = R_i_alpha_total_exp[alpha]
mmvt_Ri = main_data_sample.R_i_alpha[alpha]
compare_dicts(mmvt_Ri_exp, mmvt_Ri)
return