def test_strict_logger(self):
log = logger.StrictLogger()
# default values
self.assertEqual(log.log_level, log.WARNING)
self.assertEqual(log.log_data, False)
# skip info and debug
log.i("Nothing - Info")
# Errors on warning and above
with self.assertRaises(RuntimeError,
msg="w triggered RuntimeError via logger"):
log.w("w triggered RuntimeError via logger")
with self.assertRaises(RuntimeError,
msg="e triggered RuntimeError via logger"):
log.e("e triggered RuntimeError via logger")
# Uo prefix
uo = DummyUO()
with self.assertRaises(RuntimeError, msg="w triggered RuntimeError"):
uo.write_to_strict_logger(log.WARNING,
"DummyUnitOp: w triggered RuntimeError")
# skip adding any data
data_tree = dict()
log.set_data_tree("MainTree", data_tree)
log.d_data(data_tree, "bo1", 10)
log.i_data(data_tree, "bo2", 20)
self.assertTrue(len(data_tree.keys()) == 0)
# ensure empty function and call function for 100 % coverage
self.assert_empty_function(log._on_data_stored)
log._on_data_stored(0, dict(), "", "")
def run_emg_fixed_relative_width(self,
t,
rt_mean_list,
sigma_list,
skew_list,
cutoff,
ignore_logger=True) -> None:
for skew in skew_list:
for sigma in sigma_list:
for rt_mean in rt_mean_list:
if rt_mean < 1 / skew: # peak max would be at t < 0
continue
p = self.get_emg_pdf_with_cutoff(t, rt_mean, sigma, skew,
cutoff)
pd = pdf.ExpModGaussianFixedRelativeWidth(
t,
sigma_relative=sigma / rt_mean,
tau_relative=1 / skew / rt_mean)
if ignore_logger:
pd.log.log_level = pd.log.ERROR + 10
else:
pd.log = logger.StrictLogger()
pd.cutoff_relative_to_max = cutoff
pd.update_pdf(rt_mean=rt_mean)
np.testing.assert_array_almost_equal(pd.get_p(), p, 3)
pd.update_pdf(rt_mean=rt_mean / 2)
p = self.get_emg_pdf_with_cutoff(t, rt_mean / 2, sigma / 2,
skew * 2, cutoff)
np.testing.assert_array_almost_equal(pd.get_p(), p, 3)
pd.update_pdf(rt_mean=rt_mean / 2, skew=skew / 3)
p = self.get_emg_pdf_with_cutoff(t, rt_mean / 2, sigma / 2,
skew / 3, cutoff)
np.testing.assert_array_almost_equal(pd.get_p(), p, 3)
def setUp(self) -> None:
self.logger = logger.StrictLogger()
self.logger.log_data = True
self.logger.log_level = self.logger.DEBUG
self.uo_id = "combo_uo"
self.gui_title = "Parent UO"
self.t = np.linspace(0, 100, 1000)
self.child_uo_list = [
MockUpUnitOperation(self.t, "uo_1", "Unit Operation 1"),
MockUpUnitOperation(self.t, "uo_2", "Unit Operation 2"),
MockUpUnitOperation(self.t, "uo_3", "Unit Operation 3")
]
self.child_uo_list[0].add_to_c = 0.1
self.child_uo_list[1].add_to_c = 0.01
self.child_uo_list[2].add_to_c = 0.001
self.uo_combo = special_uo.ComboUO(self.t, self.child_uo_list,
self.uo_id, self.gui_title)
class DummyUO:
_log_entity_id = "DummyUnitOp"
default_logger = logger.DefaultLogger()
data_storing_logger = logger.DataStoringLogger()
strict_logger = logger.StrictLogger()
def write_to_default_logger(self, lvl, msg):
self.default_logger.log(lvl, msg)
def write_to_data_storing_logger(self, lvl, msg):
self.data_storing_logger.log(lvl, msg)
def write_to_strict_logger(self, lvl, msg):
self.strict_logger.log(lvl, msg)
def data_write_to_data_storing_logger(self, tree, lvl, key, value):
if lvl == self.data_storing_logger.INFO:
self.data_storing_logger.i_data(tree, key, value)
else:
self.data_storing_logger.d_data(tree, key, value)
def run_tanks_in_series(self,
t,
rt_mean_list,
n_tank_list,
cutoff,
ignore_logger=True) -> None:
for n_tanks in n_tank_list:
for rt_mean in rt_mean_list:
p = self.get_tanks_in_series_pdf_with_cutoff(
t, rt_mean, n_tanks, cutoff)
pd = pdf.TanksInSeries(t, n_tanks)
if ignore_logger:
pd.log.log_level = pd.log.ERROR + 10
else:
pd.log = logger.StrictLogger()
pd.cutoff_relative_to_max = cutoff
pd.update_pdf(rt_mean=rt_mean)
np.testing.assert_array_almost_equal(pd.get_p(), p, 3)
pd.update_pdf(rt_mean=rt_mean / 2)
p = self.get_tanks_in_series_pdf_with_cutoff(
t, rt_mean / 2, n_tanks, cutoff)
np.testing.assert_array_almost_equal(pd.get_p(), p, 3)
def run_gauss_fixed_relative_with(self,
t,
rt_mean_list,
sigma_list,
cutoff,
ignore_logger=True) -> None:
for rt_mean in rt_mean_list:
for sigma in sigma_list:
p = self.get_gauss_pdf_with_cutoff(t, rt_mean, sigma, cutoff)
pd = pdf.GaussianFixedRelativeWidth(t,
relative_sigma=sigma /
rt_mean,
cutoff=cutoff)
if ignore_logger:
pd.log.log_level = pd.log.ERROR + 10
else:
pd.log = logger.StrictLogger()
pd.update_pdf(rt_mean=rt_mean)
np.testing.assert_array_equal(pd.get_p(), p)
pd.update_pdf(rt_mean=rt_mean / 2)
p = self.get_gauss_pdf_with_cutoff(t, rt_mean / 2, sigma / 2,
cutoff)
np.testing.assert_array_almost_equal(pd.get_p(), p, 5)