def test_unique_timepoints(self):
t_all = Helpers.get_unique_timepoints([
StaticData.time_series_1,
StaticData.time_series_2,
StaticData.measurements_w_errors,
StaticData.measurements_wo_errors,
])
assert len(t_all) == 4
assert all(np.equal(t_all, np.array([1, 2, 3, 4])))
def test_parameter_collections(self):
"""
Methods related to parameter distributions from MC sampling or parameter scanning studies
"""
parameter_collection_not_ok = {'p1': [1, 2, 3], 'p2': [10, 20, 30, 40]}
parameter_collection_ok = {'p1': [1, 2, 3], 'p2': [10, 20, 30]}
# The parameters shall all have the same length
assert Helpers.get_parameters_length(parameter_collection_ok) == 3
# The parameters are not allowed to have different lengths
with pytest.raises(ValueError):
Helpers.get_parameters_length(parameter_collection_not_ok)
# Parameter collections can be sliced for, e.g. get predictions for a particular slice
parameter_slices = Helpers.split_parameters_distributions(
parameter_collection_ok)
for parameter_slice in parameter_slices:
assert list(parameter_slice.keys()) == list(
parameter_collection_ok.keys())
def test_utils_for_datatypes(self):
# To check whether all measurements in a list of those hve errors or not
assert Helpers.all_measurements_have_errors([
StaticData.measurements_wo_errors, StaticData.measurements_w_errors
]) == False
assert Helpers.all_measurements_have_errors([
StaticData.measurements_w_errors, StaticData.measurements_w_errors
]) == True
assert Helpers.all_measurements_have_errors([
StaticData.measurements_wo_errors,
StaticData.measurements_wo_errors
]) == False
# Get the joint time vector of several TimeSeries objects
actual = Helpers.get_unique_timepoints([
StaticData.measurements_wo_errors, StaticData.measurements_w_errors
])
for _actual, _expected in zip(actual, np.array([1., 2., 3., 4.])):
assert _actual == _expected
# Extract a specific TimeSeries from a list
timeseries_list = [
StaticData.measurements_wo_errors, StaticData.measurements_w_errors
]
assert isinstance(
Helpers.extract_time_series(timeseries_list,
replicate_id='1st',
name='M1'), TimeSeries)
# In case not match is found, the method returns None
with pytest.warns(UserWarning):
assert Helpers.extract_time_series(
timeseries_list,
replicate_id='2nd',
name='M1',
no_extraction_warning=True) is None
# More than one match is found
with pytest.raises(ValueError):
Helpers.extract_time_series(timeseries_list * 2,
replicate_id='1st',
name='M1')
def test_unique_ids_must_be_list_or_dict(self):
with pytest.raises(TypeError):
Helpers.has_unique_ids(('a01', 'b01'))
def test_unique_ids_not_ok(self, not_ok_ids):
"""
To ensure that ids (replicate_ids, states, etc) are case-insensitive unique
"""
assert Helpers.has_unique_ids(not_ok_ids) == False
def test_bounds_to_floats(self):
int_bounds = [(0, 1), (2, 3)]
float_bounds = Helpers.bounds_to_floats(int_bounds)
for _bounds in float_bounds:
assert isinstance(_bounds[0], float)
assert isinstance(_bounds[1], float)