def test_parameter_numpy_operations():
"""Operators work like a float"""
parm1 = Parameter(value=1)
parm1_neg = Parameter(value=-1)
parm2 = Parameter(value=2)
parm3 = Parameter(value=3)
parm3_5 = Parameter(value=3.5)
assert parm1 == 1
assert parm1 != 2
assert -parm1 == -1
assert abs(parm1_neg) == 1
assert int(parm1) == 1
assert np.allclose(float(parm1), 1)
assert np.allclose(parm1 + parm2, 3)
assert np.allclose(parm3 - parm2, 1)
assert np.allclose(parm3 * parm2, 6)
assert np.allclose(parm3 / parm2, 1.5)
assert parm3 // parm2 == 1
assert np.trunc(parm3_5) == 3
assert parm2 % 2 == 0
assert 2 % parm2 == 0
assert divmod(parm3, 2) == (1, 1)
assert divmod(3, parm2) == (1, 1)
assert np.allclose(parm3**parm2, 9)
assert parm3 > parm2
assert parm3 >= parm2
assert parm1 < parm2
assert parm1 <= parm2
def setter(that_self, value: set_type):
if value is None and not self._allow_none:
raise Exception(
f"Property '{name}' of '{cls.__name__}' is not allowed to set to None.")
if self._is_parameter and value is not None:
if self._is_parameter_value and isinstance(value, str):
value = Parameter(full_label=str(value))
elif self._is_parameter_list and all([isinstance(v, str) for v in value]):
value = [Parameter(full_label=str(v)) for v in value]
elif self._is_parameter_dict and all([isinstance(v, str) for v in value.values()]):
for k, v in value.items():
value[k] = Parameter(full_label=v)
setattr(that_self, f'_{self._name}', value)
def test_parameter_set_from_group():
"""Parameter extracted from group has correct values"""
group = load_parameters(
"foo:\n - [\"1\", 123,{non-negative: true, min: 10, max: 8e2, vary: true, expr:'2'}]",
format_name="yml_str",
)
parameter = Parameter(full_label="foo.1")
parameter.set_from_group(group=group)
assert parameter.value == 123
assert parameter.non_negative is True
assert np.allclose(parameter.minimum, 10)
assert np.allclose(parameter.maximum, 800)
assert parameter.vary is True
# Set to None since value and expr were provided?
assert parameter.expression is None
def setter(self, value: model_property.glotaran_property_type): # type: ignore[name-defined]
if value is None and not model_property._allow_none:
raise ValueError(
f"Property '{model_property._name}' of '{cls.__name__}' "
"is not allowed to set to None."
)
if value is not None and model_property.glotaran_is_parameter_property:
if model_property.glotaran_is_scalar_property and not isinstance(value, Parameter):
value = Parameter(full_label=str(value))
elif model_property.glotaran_is_sequence_property and all(
map(lambda v: not isinstance(v, Parameter), value)
):
value = [Parameter(full_label=str(v)) for v in value]
elif model_property.glotaran_is_mapping_property and all(
map(lambda v: not isinstance(v, Parameter), value.values())
):
value = {k: Parameter(full_label=str(v)) for k, v in value.items()}
setattr(self, f"_{model_property._name}", value)
def test_parameter_pickle(tmpdir):
parameter = Parameter("testlabel", "testlabelfull", "testexpression", 1, 2, True, 42, False)
with open(tmpdir.join("test_param_pickle"), "wb") as f:
pickle.dump(parameter, f)
with open(tmpdir.join("test_param_pickle"), "rb") as f:
pickled_parameter = pickle.load(f)
assert parameter == pickled_parameter
def test_parameter_dict_roundtrip():
param = Parameter(
label="foo",
full_label="bar.foo",
expression="1",
maximum=2,
minimum=1,
non_negative=True,
value=42,
vary=False,
)
param_dict = param.as_dict()
param_from_dict = Parameter.from_dict(param_dict)
assert param.label == param_from_dict.label
assert param.full_label == param_from_dict.full_label
assert param.expression == param_from_dict.expression
assert param.maximum == param_from_dict.maximum
assert param.minimum == param_from_dict.minimum
assert param.non_negative == param_from_dict.non_negative
assert param.value == param_from_dict.value
assert param.vary == param_from_dict.vary
def test_parameter_repr():
"""Repr creates code to recreate the object."""
result = Parameter(label="foo",
value=1.0,
expression="$foo.bar",
vary=False)
result_short = Parameter(label="foo", value=1, expression="$foo.bar")
expected = "Parameter(label='foo', value=1.0, expression='$foo.bar', vary=False)"
assert result == result_short
assert result.vary == result_short.vary
assert result.__repr__() == expected
assert result_short.__repr__() == expected
def test_transform_expression(case):
expression, wanted_parameters = case
parameter = Parameter(expression=expression)
assert parameter.transformed_expression == wanted_parameters
def test_non_negative():
notnonneg = Parameter(value=1, non_negative=False)
valuenotnoneg, _, _ = notnonneg.get_value_and_bounds_for_optimization()
assert np.allclose(1, valuenotnoneg)
notnonneg.set_value_from_optimization(valuenotnoneg)
assert np.allclose(1, notnonneg.value)
nonneg1 = Parameter(value=1, non_negative=True)
value1, _, _ = nonneg1.get_value_and_bounds_for_optimization()
assert not np.allclose(1, value1)
nonneg1.set_value_from_optimization(value1)
assert np.allclose(1, nonneg1.value)
nonneg2 = Parameter(value=2, non_negative=True)
value2, _, _ = nonneg2.get_value_and_bounds_for_optimization()
assert not np.allclose(2, value2)
nonneg2.set_value_from_optimization(value2)
assert np.allclose(2, nonneg2.value)
nonnegminmax = Parameter(value=5, minimum=3, maximum=6, non_negative=True)
value5, minimum, maximum = nonnegminmax.get_value_and_bounds_for_optimization(
)
assert not np.allclose(5, value5)
assert not np.allclose(3, minimum)
assert not np.allclose(6, maximum)
def test_parameter_label_error_wrong_label_pattern(label: str | int | float):
"""Error if label can't be casted to a valid label str"""
with pytest.raises(ValueError,
match=f"'{label}' is not a valid group label."):
Parameter(label=label) # type:ignore[arg-type]
def test_transform_expression(case):
expression, wanted_parameters = case
parameter = Parameter(expression=expression)
assert parameter.transformed_expression == wanted_parameters
# just for the test coverage so the if condition is wrong
assert parameter.transformed_expression == wanted_parameters
def test_parameter_minimum_not_numeric_error():
"""Error if minimum isn't numeric."""
with pytest.raises(TypeError, match="Parameter minimum must be numeric"):
Parameter(minimum="foo") # type:ignore[arg-type]
def test_parameter_value_not_numeric_error():
"""Error if value isn't numeric."""
with pytest.raises(TypeError, match="Parameter value must be numeric"):
Parameter(value="foo") # type:ignore[arg-type]
def test_parameter_label_always_str_or_None(label: str | int | None,
expected: str | None):
"""Parameter.label is always a string or None"""
parameter = Parameter(label=label) # type:ignore[arg-type]
assert parameter.label == expected
