def test_to_excel_fails(self, trained_model, primary, tmpdir):
crm = trained_model(primary)
with pytest.raises(TypeError):
crm.to_excel()
crm2 = CRM(primary)
with pytest.raises(ValueError):
crm2.to_excel(tmpdir + '/test.xlsx')
def test_init_fails(self, primary, tau_selection, constraints):
with pytest.raises(TypeError):
CRM(primary='yes',
tau_selection=tau_selection,
constraints=constraints)
with pytest.raises(ValueError):
CRM(primary=primary,
tau_selection=tau_selection,
constraints='negative')
with pytest.raises(ValueError):
CRM(primary=primary,
tau_selection='per-Bob',
constraints=constraints)
def _trained_model(*args, **kwargs):
crm = CRM(*args, **kwargs)
crm.injection, crm.production, crm.time = reservoir_simulation_data
crm.gains = np.genfromtxt(data_dir + 'gains.csv', delimiter=',')
if crm.tau_selection == 'per-pair':
crm.tau = np.genfromtxt(data_dir + 'taus_per-pair.csv',
delimiter=',')
else:
crm.tau = np.genfromtxt(data_dir + 'taus.csv', delimiter=',')
crm.gains_producer = np.genfromtxt(data_dir + 'gain_producer.csv',
delimiter=',')
crm.tau_producer = np.genfromtxt(data_dir + 'tau_producer.csv',
delimiter=',')
return crm
def test_fit_initial_guess(self, reservoir_simulation_data, primary,
tau_selection, constraints):
injection, production, time = reservoir_simulation_data
crm = CRM(primary, tau_selection, constraints)
crm.set_rates(production, injection, time)
x0 = crm._get_initial_guess(tau_selection)
for random in (True, False):
crm.fit(production,
injection,
time,
random=random,
initial_guess=x0,
options={'maxiter': 100})
def test_fit_fails(self, reservoir_simulation_data, primary, tau_selection,
constraints):
crm = CRM(primary, tau_selection, constraints)
injection, production, time = reservoir_simulation_data
with pytest.raises(TypeError):
crm.fit(production)
with pytest.raises(TypeError):
crm.fit(production, injection)
with pytest.raises(ValueError):
crm.fit(production[:-1], injection, time)
with pytest.raises(ValueError):
crm.fit(production, injection[:-1], time)
with pytest.raises(ValueError):
crm.fit(
production,
injection,
time[:-1],
)
def test_init(self, primary, tau_selection, constraints):
CRM(primary, tau_selection, constraints)
def test_fit_parallel(self, reservoir_simulation_data, primary,
tau_selection, constraints):
injection, production, time = reservoir_simulation_data
crm = CRM(primary, tau_selection, constraints)
crm.fit(production, injection, time, num_cores=4)