def test_shape_conversion_exponential():
"""Exponential Distribution Test: convert shape from u_kn to u_ijn."""
test = exponential_distributions.ExponentialTestCase(rates)
x_n, u_kn, origin = test.sample(np.array([5, 6, 7]))
u_ijn, N_k = convert_ukn_to_uijn(u_kn)
x_n, u_kn, origin = test.sample(np.array([5, 5, 5]))
u_ijn, N_k = convert_ukn_to_uijn(u_kn)
x_n, u_kn, origin = test.sample(np.array([1., 1, 1.]))
u_ijn, N_k = convert_ukn_to_uijn(u_kn)
def test_exponential_mbar_free_energies():
"""Exponential Distribution Test: can MBAR calculate correct free energy differences?"""
test = exponential_distributions.ExponentialTestCase(rates)
x_n, u_kn, origin = test.sample(N_k)
u_ijn, N_k_output = convert_ukn_to_uijn(u_kn)
eq(N_k, N_k_output.values)
mbar = MBAR(u_ijn.values, N_k)
fe, fe_sigma = mbar.getFreeEnergyDifferences()
fe, fe_sigma = fe[0], fe_sigma[0]
fe0 = test.analytical_free_energies()
z = (fe - fe0) / fe_sigma
z = z[1:] # First component is undetermined.
eq(z / z_scale_factor, np.zeros(len(z)), decimal=0)
def test_exponential_mbar_xkn_squared():
"""Exponential Distribution Test: can MBAR calculate E(x_kn^2)"""
test = exponential_distributions.ExponentialTestCase(rates)
x_n, u_kn, origin = test.sample(N_k)
u_ijn, N_k_output = convert_ukn_to_uijn(u_kn)
eq(N_k, N_k_output.values)
mbar = MBAR(u_ijn.values, N_k)
x_kn = convert_xn_to_x_kn(x_n) ** 2.0
x_kn = x_kn.values # Convert to numpy for MBAR
x_kn[np.isnan(x_kn)] = 0.0 # Convert nans to 0.0
mu, sigma = mbar.computeExpectations(x_kn)
mu0 = test.analytical_x_squared()
z = (mu0 - mu) / sigma
eq(z / z_scale_factor, np.zeros(len(z)), decimal=0)
def generate_exp(rates=np.array([1.0, 2.0, 3.0, 4.0])): # Rates, e.g. Lambda
return "Exponentials", exponential_distributions.ExponentialTestCase(rates)
def test_exponential_samples():
"""Exponential Distribution Test: draw samples via test object."""
test = exponential_distributions.ExponentialTestCase(rates)
x_n, u_kn, origin = test.sample(np.array([5, 6, 7]))
x_n, u_kn, origin = test.sample(np.array([5, 5, 5]))
x_n, u_kn, origin = test.sample(np.array([1., 1, 1.]))
def test_analytical_exponential():
"""Exponential Distribution Test: generate test object and calculate analytical results."""
test = exponential_distributions.ExponentialTestCase(rates)
mu = test.analytical_means()
variance = test.analytical_variances()
f_k = test.analytical_free_energies()
