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 test_Data_sample_calculate_kinetics(toy_mmvt_model):
data_sample = common_analyze.Data_sample(toy_mmvt_model)
fill_out_simple_statistics(toy_mmvt_model, data_sample)
data_sample.calculate_thermodynamics()
data_sample.calculate_kinetics()
assert data_sample.MFPTs is not None
assert data_sample.k_off is not None
return
def test_Data_sample_calculate_thermodynamics(toy_mmvt_model):
data_sample = common_analyze.Data_sample(toy_mmvt_model)
fill_out_simple_statistics(toy_mmvt_model, data_sample)
data_sample.calculate_thermodynamics()
# assert all values of p_i are equal
assert np.all(np.isclose(data_sample.p_i, data_sample.p_i[0]))
assert np.all(np.isclose(data_sample.free_energy_profile, 0.0))
return
def test_Data_sample_compute_rate_matrix(toy_mmvt_model):
data_sample = common_analyze.Data_sample(toy_mmvt_model)
n = toy_mmvt_model.num_milestones
data_sample.N_ij = np.zeros((n, n))
data_sample.R_i = np.zeros(n)
for i in range(n):
data_sample.R_i[i] = 0.5
if i > 0:
data_sample.N_ij[i, i - 1] = 10
if i < n - 1:
data_sample.N_ij[i, i + 1] = 10
data_sample.compute_rate_matrix()
for i in range(n):
if i > 0:
assert data_sample.Q[i, i - 1] == 10 / 0.5
else:
assert data_sample.K[i, i + 1] == 1.0
if i < n - 1:
assert data_sample.Q[i, i + 1] == 10 / 0.5
else:
assert data_sample.K[i, i - 1] == 1.0
if (i > 0) and (i < n - 1):
assert data_sample.Q[i, i] == -20 / 0.5
assert data_sample.K[i, i + 1] == 0.5
assert data_sample.K[i, i - 1] == 0.5
else:
assert data_sample.Q[i, i] == -10 / 0.5
assert data_sample.K[i, i] == 0.0
for j in range(n):
if (j == i) or (j == i - 1) or (j == i + 1):
continue
else:
assert data_sample.Q[i, j] == 0.0
assert data_sample.K[i, j] == 0.0
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