def test_N_conflict_produces_error_message(self):
# create test data
data = DataStructure()
x = np.zeros([2])
y = np.zeros([2,2])
data.add_data_set(x,y)
options = setup_simulation_options(nsimu = int(1000))
ms = setup_model_settings(N = 1)
# calculate dependencies
with self.assertRaises(SystemExit, msg = 'Conflicting N'):
ms._check_dependent_model_settings(data, options)
def test_value_sigma2_wrt_nsos_raises_error(self):
# create test data
data = DataStructure()
x = np.zeros([2])
y = np.zeros([2])
data.add_data_set(x,y)
options = setup_simulation_options(nsimu = int(1000))
ms = setup_model_settings(sigma2 = [1,2])
# calculate dependencies
ms._check_dependent_model_settings(data, options)
nsos = 1
with self.assertRaises(SystemExit, msg = 'sigma2 should not be larger than nsos'):
ms._check_dependent_model_settings_wrt_nsos(nsos = nsos)
def test_default_S20_dependent(self):
# create test data
data = DataStructure()
x = np.zeros([2])
y = np.zeros([2,2])
data.add_data_set(x,y)
options = setup_simulation_options(nsimu = int(1000))
ms = setup_model_settings()
# calculate dependencies
ms._check_dependent_model_settings(data, options)
self.assertAlmostEqual(ms.S20, ms.sigma2, msg='default is sigma2')
def test_default_sigma2_dependent_for_non_default_N0(self):
# create test data
data = DataStructure()
x = np.zeros([2])
y = np.zeros([2,2])
data.add_data_set(x,y)
options = setup_simulation_options(nsimu = int(1000))
ms = setup_model_settings(N0 = 1)
# calculate dependencies
ms._check_dependent_model_settings(data, options)
self.assertEqual(ms.sigma2, np.ones(ms.nbatch), msg='default is ones(ms.nbatch)');
def test_N_calc_from_data(self):
# create test data
data = DataStructure()
x = np.zeros([2])
y = np.zeros([2,2])
data.add_data_set(x,y)
options = setup_simulation_options(nsimu = int(1000))
ms = setup_model_settings()
# calculate dependencies
ms._check_dependent_model_settings(data, options)
self.assertTrue(np.array_equal(ms.N, np.array([2])), msg = 'N should equal total number of rows in each y set')
def test_nbatch_calc_from_data_y_2d(self):
# create test data
data = DataStructure()
x = np.zeros([2])
y = np.zeros([2,2])
data.add_data_set(x,y)
options = setup_simulation_options(nsimu = int(1000))
ms = setup_model_settings()
# calculate dependencies
ms._check_dependent_model_settings(data, options)
self.assertEqual(ms.nbatch, 1, msg = 'nbatch should equal number of calls to ''add_data_set''')
def test_print_these_none(self):
data = DataStructure()
x = np.zeros([2])
y = np.zeros([2])
data.add_data_set(x,y)
options = SimulationOptions()
ms = ModelSettings()
ms.define_model_settings()
ms._check_dependent_model_settings(data, options)
ms._check_dependent_model_settings_wrt_nsos(nsos = 1)
print_these = ms.display_model_settings(print_these = None)
self.assertEqual(print_these, ['sos_function', 'model_function', 'sigma2', 'N', 'N0', 'S20', 'nsos', 'nbatch'], msg = 'Default print keys')
def test_print_these_not_none(self):
data = DataStructure()
x = np.zeros([2])
y = np.zeros([2])
data.add_data_set(x,y)
options = SimulationOptions()
ms = ModelSettings()
ms.define_model_settings()
ms._check_dependent_model_settings(data, options)
ms._check_dependent_model_settings_wrt_nsos(nsos = 1)
print_these = ms.display_model_settings(print_these = ['model_function'])
self.assertEqual(print_these, ['model_function'], msg = 'Specified print keys')
# -------------------------------------------
def test_size_sigma2_wrt_nsos_for_non_default_sigma2_raises_error(self):
# create test data
data = DataStructure()
x = np.zeros([2])
y = np.zeros([2])
data.add_data_set(x,y)
options = setup_simulation_options(nsimu = int(1000))
ms = setup_model_settings(sigma2 = [1,2])
# calculate dependencies
ms._check_dependent_model_settings(data, options)
nsos = 3
with self.assertRaises(SystemExit, msg = 'sigma2 size mismatch'):
ms._check_dependent_model_settings_wrt_nsos(nsos = nsos)
def test_size_N_wrt_nsos(self):
# create test data
data = DataStructure()
x = np.zeros([2])
y = np.zeros([2])
data.add_data_set(x,y)
options = setup_simulation_options(nsimu = int(1000))
ms = setup_model_settings()
# calculate dependencies
ms._check_dependent_model_settings(data, options)
nsos = 2
ms._check_dependent_model_settings_wrt_nsos(nsos = nsos)
self.assertEqual(len(ms.N), nsos, msg = 'length of N should equal number of elements returned from sos function')
def test_default_sigma2_dependent_for_non_default_N0_S20_multiple_data_sets(self):
# create test data
data = DataStructure()
x = np.zeros([2])
y = np.zeros([2,2])
data.add_data_set(x,y)
y = np.zeros([3,2])
data.add_data_set(x,y)
options = setup_simulation_options(nsimu = int(1000))
ms = setup_model_settings(N0 = 1, S20 = 1)
# calculate dependencies
ms._check_dependent_model_settings(data, options)
comp = np.linalg.norm(ms.sigma2 - ms.S20)
self.assertAlmostEqual(comp, 0, msg='default is S20')
def test_value_N_wrt_nsos_for_non_default_N_and_nsos_equal_1(self):
# create test data
data = DataStructure()
x = np.zeros([2])
y = np.zeros([2])
data.add_data_set(x,y)
y = np.zeros([4,2])
data.add_data_set(x,y)
options = setup_simulation_options(nsimu = int(1000))
ms = setup_model_settings(N = [2,4])
# calculate dependencies
ms._check_dependent_model_settings(data, options)
nsos = 1
ms._check_dependent_model_settings_wrt_nsos(nsos = nsos)
self.assertEqual(ms.N, 6, msg = 'length of N should equal number of elements returned from sos function')
xdata = data.user_defined_object[0]
theta = {
'Gc': t[0],
'Ge': t[1],
'lam_max': t[2],
'eta': t[3],
'gamma': t[4]
}
stress = nonaffine_hyperelastic_model(
theta, xdata[:, 1]) + linear_viscoelastic_model(
theta, xdata[:, 1], xdata[:, 0])
return stress
# plot wrt time
pdata = DataStructure()
pdata.add_data_set(x=time,
y=stress,
user_defined_object=vhbdata['data']['xdata'][0][0])
mcstat.PI.setup_prediction_interval_calculation(results=results,
data=pdata,
modelfunction=predmodelfun,
burnin=burnin)
mcstat.PI.generate_prediction_intervals(nsample=500, calc_pred_int=True)
# plot prediction intervals
mcstat.PI.plot_prediction_intervals(adddata=True)
plt.xlabel('Time (sec)', fontsize=22)
plt.xticks(fontsize=22)
plt.ylabel('Nominal Stress (kPa)', fontsize=22)
plt.yticks(fontsize=22)
plt.xlim([0, time[-1]])
def basic_data_structure():
DS = DataStructure()
x = np.random.random_sample(size=(100, 1))
y = np.random.random_sample(size=(100, 1))
DS.add_data_set(x=x, y=y)
return DS
