def test_manual_creation(self):
with reraise_traits_notification_exceptions():
group = self.sim_group_maker(size=3)
self.assertIsInstance(group, SimulationGroup)
self.assertEqual(group.size, 3)
self.assertIsInstance(group.center_point_simulation, self.sim_class)
self.assertFalse(group.has_run)
self.assertEqual(group.run_status, MULTI_SIM_RUNNER_CREATED)
# test data dataframe
df = group.group_data
self.assertIsInstance(df, pd.DataFrame)
expected_columns = {
'binding_model.sma_ka[1]', 'pool_volume (CV)',
'pool_concentration (g/L)', 'step_yield (%)', SIM_COL_NAME
}
components = self.sim.product.product_component_names
expected_purities = {
"purity: {} (%)".format(comp)
for comp in components
}
expected_columns = expected_columns.union(expected_purities)
self.assertEqual(set(df.columns), expected_columns)
expected_sims = {'Sim 2', 'Sim 1', 'Sim 0'}
self.assertEqual(set(df[SIM_COL_NAME]), expected_sims)
expected_index = range(len(expected_sims))
self.assertEqual(list(df.index), expected_index)
param_name = 'binding_model.sma_ka[1]'
expected_bind_models = pd.Series([0.001, 0.01, 0.1], name=param_name)
assert_series_equal(df[param_name], expected_bind_models)
# No output set yet
self.assertTrue(np.all(np.isnan(df.iloc[:, 2:])))
def test_compute_summary_if_empty_filtered(self):
analyzer = self.analyzer_klass(source_df=self.df)
# Set filter so there is no filtered data:
analyzer.filter_exp = "a > 100"
with reraise_traits_notification_exceptions():
summary = analyzer.compute_summary()
assert_frame_equal(summary, pd.DataFrame([]))
def test_update_starting_sim_from_name(self):
""" Changing starting point sim should trigger a recomputation of cp
"""
optim_builder = self.make_optimizer_builder()
optim_builder.experiment_selected = ['Run_1']
self.assertEqual(optim_builder.starting_point_simulation_name,
'Sim: Run_1')
self.assertEqual(len(optim_builder.starting_point_simulations), 1)
first_cp = optim_builder.starting_point_simulations[0]
self.assertIsNot(first_cp, self.sim1)
assert_has_traits_almost_equal(first_cp, self.sim1, check_type=False)
with self.assertTraitChanges(optim_builder,
"starting_point_simulations", 1):
optim_builder.starting_point_simulation_name = 'Sim: Run_2'
first_cp = optim_builder.starting_point_simulations[0]
self.assertEqual(len(optim_builder.starting_point_simulations), 1)
# The target experiment is still Run_1 so the resulting sim ~ sim1
assert_has_traits_almost_equal(first_cp, self.sim1, check_type=False)
self.assertIsInstance(first_cp, LazyLoadingSimulation)
# But several quantities are pulled from the specified starting point
self.assertEqual(first_cp.method.method_steps[0].name,
self.sim2.method.method_steps[0].name)
self.assertEqual(first_cp.method.method_steps[-1].name,
self.sim2.method.method_steps[-1].name)
# Choosing a different name
with self.assertTraitDoesNotChange(optim_builder,
"starting_point_simulations"):
with self.assertRaises(KeyError):
# Reraise the exception because it happens in listener
with reraise_traits_notification_exceptions():
optim_builder.starting_point_simulation_name = \
"DOESN'T EXIST!"
def test_change_cost_data(self):
""" Changing cost data should trigger update of plot data and colorbar
range. This mimics what happens when the weights of the cost function
have changed.
"""
initial_plot_data_dict = self.explorer.cost_plot_data.arrays.copy()
colorbar_range = self.explorer.colorbar.index_mapper.range
initial_low = colorbar_range.low
initial_high = colorbar_range.high
new_cost_data = self.optimizer.cost_data.copy()
cols_to_change = [
ALL_COST_COL_NAME, 'binding_model.sma_lambda',
'column.resin.ligand_density'
]
shift = 2
new_cost_data[cols_to_change] += shift
# Don't silence exception raised when listening to changes to cost_data
with reraise_traits_notification_exceptions():
self.optimizer.cost_data = new_cost_data
new_plot_data = self.explorer.cost_plot_data.arrays.copy()
for key, new_arr in new_plot_data.items():
assert_array_almost_equal(new_arr,
initial_plot_data_dict[key] + shift)
colorbar_range = self.explorer.colorbar.index_mapper.range
new_low = colorbar_range.low
new_high = colorbar_range.high
self.assertAlmostEqual(new_low, initial_low + shift)
self.assertAlmostEqual(new_high, initial_high + shift)
def assert_old_file_read(filename, reference_task, ignore=(), eps=1e-9):
with reraise_traits_notification_exceptions():
obj, legacy = load_object(io_data_path(filename))
assert_has_traits_almost_equal(obj.project,
reference_task.project,
ignore=ignore,
eps=eps)
assert_true(legacy)
def test_change_product_assays_list(self):
view = self._get_model_view()
original = view.product_assays
try:
self.assertEqual(len(view.product_assays), 3)
self.assertEqual(len(self.model.product_component_assay_values), 3)
with reraise_traits_notification_exceptions():
view.product_assays = view.product_assays[1:]
self.assertEqual(len(self.model.product_component_assay_values), 2)
with reraise_traits_notification_exceptions():
view.product_assays = []
self.assertEqual(len(self.model.product_component_assay_values), 0)
finally:
# Reset to make ready for next steps
view.product_assays = original
def test_change_chemical_component_list(self):
view = self._get_model_view()
original = self.model.chemical_components
try:
self.assertEqual(len(view.chemical_components), 3)
self.assertEqual(len(self.model.chemical_components), 3)
with reraise_traits_notification_exceptions():
view.chemical_components = view.chemical_components[1:]
self.assertEqual(len(self.model.chemical_components), 2)
with reraise_traits_notification_exceptions():
view.chemical_components = []
self.assertEqual(len(self.model.chemical_components), 0)
finally:
# Reset to initial value
self.model.chemical_components = original
def test_change_model_chem_components(self):
view = self._get_model_view()
self.assertEqual(len(self.model.chemical_components), 3)
with reraise_traits_notification_exceptions():
self.model.chemical_components = self.model.chemical_components[1:]
# The view doesn't change from model changes until the
# update_chemical_components is explicitely called:
self.assertEqual(len(view.chemical_components), 3)
view.update_chemical_components()
self.assertEqual(len(view.chemical_components), 2)
def test_filter_transformation(self):
df = self.df
with self.assertRaises(UndefinedVariableError):
with reraise_traits_notification_exceptions():
self.analyzer_klass(source_df=df, filter_exp="A > 2")
analysis = self.analyzer_klass(
source_df=df, filter_exp="A > 2",
filter_transformation=lambda x: x.lower()
)
filtered = df.iloc[3:, :]
assert_frame_equal(analysis.source_df, df)
assert_frame_equal(analysis.filtered_df, filtered)
assert_frame_equal(analysis.displayed_df, filtered)
def test_bad_filter_with_raise(self):
""" If filter set to a bad value, and raise behavior, error raised in
listener.
"""
df = self.df2
analyzer = self.analyzer_klass(source_df=df,
filter_error_handling="raise")
self.assertEqual(analyzer.filter_exp, "")
assert_frame_equal(analyzer.filtered_df, analyzer.source_df)
# Request that involves an invalid expression raises a syntax error
# inside the listener:
with reraise_traits_notification_exceptions():
with self.assertRaises(SyntaxError):
analyzer.filter_exp = "a > "
with self.assertRaises(UndefinedVariableError):
analyzer.filter_exp = "c > 1"
def test_change_model_assays(self):
view = self._get_model_view()
original = view.product_assays
try:
self.assertEqual(len(self.model.product_component_assay_values), 3)
self.assertEqual(len(view.product_assays), 3)
with reraise_traits_notification_exceptions():
self.model.product_component_assay_values = \
self.model.product_component_assay_values[1:]
# The view doesn't change from model changes until the
# update_chemical_components is explicitely called:
self.assertEqual(len(view.product_assays), 3)
view.update_product_assays()
self.assertEqual(len(view.product_assays), 2)
finally:
view.product_assays = original
def test_change_cost_data(self):
""" Changing cost data should trigger update of plot data.
Mimics what happens when the weights of the cost function have changed.
"""
explorer = self.explorer
explorer.x_axis_param = "binding_model.sma_lambda"
initial_plot_data_dict = self.explorer.cost_plot_data.arrays.copy()
new_cost_data = self.optimizer.cost_data.copy()
cols_to_change = [
ALL_COST_COL_NAME, 'binding_model.sma_lambda',
'column.resin.ligand_density'
]
shift = 2.0
new_cost_data[cols_to_change] += shift
with reraise_traits_notification_exceptions():
self.optimizer.cost_data = new_cost_data
new_plot_data = self.explorer.cost_plot_data.arrays.copy()
for key, new_arr in new_plot_data.items():
assert_array_almost_equal(new_arr,
initial_plot_data_dict[key] + shift)