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391 lines (345 loc) · 13.5 KB
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"""Test the pydantic models."""
import pathlib
import re
from typing import Callable
import numpy as np
import pydantic
import pytest
import ratapi.models
@pytest.mark.parametrize(
["model", "model_name", "model_params"],
[
(ratapi.models.Background, "Background", {}),
(ratapi.models.Contrast, "Contrast", {}),
(ratapi.models.CustomFile, "Custom File", {}),
(ratapi.models.Data, "Data", {}),
(ratapi.models.DomainContrast, "Domain Contrast", {}),
(
ratapi.models.Layer,
"Layer",
{"thickness": "Test Thickness", "SLD": "Test SLD", "roughness": "Test Roughness"},
),
(ratapi.models.Parameter, "Parameter", {}),
(ratapi.models.Resolution, "Resolution", {}),
],
)
def test_default_names(model: Callable, model_name: str, model_params: dict) -> None:
"""When initialising multiple models without specifying a name, they should be given a default name with the
format: "New <model name> <integer>".
"""
model_1 = model(**model_params)
model_2 = model(**model_params)
model_3 = model(name="Given Name", **model_params)
model_4 = model(**model_params)
assert model_1.name == f"New {model_name} 1"
assert model_2.name == f"New {model_name} 2"
assert model_3.name == "Given Name"
assert model_4.name == f"New {model_name} 3"
@pytest.mark.parametrize(
["model", "model_params"],
[
(ratapi.models.Background, {}),
(ratapi.models.Contrast, {}),
(ratapi.models.ContrastWithRatio, {}),
(ratapi.models.CustomFile, {}),
(ratapi.models.Data, {}),
(ratapi.models.DomainContrast, {}),
(ratapi.models.Layer, {"thickness": "Test Thickness", "SLD": "Test SLD", "roughness": "Test Roughness"}),
(
ratapi.models.AbsorptionLayer,
{
"thickness": "Test Thickness",
"SLD_real": "Test SLD",
"SLD_imaginary": "Test SLD",
"roughness": "Test Roughness",
},
),
(ratapi.models.Parameter, {}),
(ratapi.models.Resolution, {}),
],
)
class TestModels:
def test_initialise_with_wrong_type(self, model: Callable, model_params: dict) -> None:
"""When initialising a model with the wrong type for the "name" field, we should raise a ValidationError."""
with pytest.raises(
pydantic.ValidationError,
match=f"1 validation error for {model.__name__}\nname\n Input should be a valid string",
):
model(name=1, **model_params)
def test_assignment_with_wrong_type(self, model: Callable, model_params: dict) -> None:
"""When assigning the "name" field of a model with the wrong type, we should raise a ValidationError."""
test_model = model(**model_params)
with pytest.raises(
pydantic.ValidationError,
match=f"1 validation error for {model.__name__}\nname\n Input should be a valid string",
):
test_model.name = 1
def test_initialise_with_zero_length_name(self, model: Callable, model_params: dict) -> None:
"""When initialising a model with a zero length name, we should raise a ValidationError."""
with pytest.raises(
pydantic.ValidationError,
match=f"1 validation error for {model.__name__}\nname\n String should have at least 1 character",
):
model(name="", **model_params)
def test_initialise_with_extra_fields(self, model: Callable, model_params: dict) -> None:
"""When initialising a model with unspecified fields, we should raise a ValidationError."""
with pytest.raises(
pydantic.ValidationError,
match=f"1 validation error for {model.__name__}\nnew_field\n Extra inputs are not permitted",
):
model(new_field=1, **model_params)
def test_custom_file_path_is_absolute() -> None:
"""If we use provide a relative path to the custom file model, it should be converted to an absolute path."""
relative_path = pathlib.Path("./relative_path")
custom_file = ratapi.models.CustomFile(path=relative_path)
assert custom_file.path.is_absolute()
def test_data_eq() -> None:
"""If we use the Data.__eq__ method with an object that is not a pydantic BaseModel, we should return
"NotImplemented".
"""
assert ratapi.models.Data().__eq__("data") == NotImplemented
@pytest.mark.parametrize(
"input_data",
[
(np.array([[1.0, 1.0, 1.0]])),
],
)
def test_data_dimension(input_data: np.ndarray[float]) -> None:
"""The "data" field of the "Data" model should be a two-dimensional numpy array with at least three values in the
second dimension.
"""
test_data = ratapi.models.Data(data=input_data)
assert (test_data.data == input_data).all()
@pytest.mark.parametrize(
"input_data",
[
(np.array([])),
(np.array([1.0, 1.0])),
],
)
def test_data_too_few_dimensions(input_data: np.ndarray[float]) -> None:
"""If the "data" field of the "Data" model is not a two-dimensional numpy array we should raise a
ValidationError.
"""
with pytest.raises(
pydantic.ValidationError,
match='1 validation error for Data\ndata\n Value error, "data" must have at least two dimensions',
):
ratapi.models.Data(data=input_data)
@pytest.mark.parametrize(
"input_data",
[
(np.array([[]])),
(np.array([[1.0]])),
(np.array([[1.0, 1.0]])),
],
)
def test_data_too_few_values(input_data: np.ndarray[float]) -> None:
"""If the second dimension of the array in the "data" field of the "Data" model has fewer than three values we
should raise a ValidationError.
"""
with pytest.raises(
pydantic.ValidationError,
match='1 validation error for Data\ndata\n Value error, "data" must have at least three columns',
):
ratapi.models.Data(data=input_data)
@pytest.mark.parametrize(
"input_range",
[
([1.0, 2.0]),
],
)
def test_data_ranges(input_range: list[float]) -> None:
"""The "data_range" and "simulation_range" fields of the "Data" model should contain exactly two values."""
assert ratapi.models.Data(data_range=input_range).data_range == input_range
assert ratapi.models.Data(simulation_range=input_range).simulation_range == input_range
@pytest.mark.parametrize(
"input_range",
[
([]),
([1.0]),
([1.0, 2.0, 3.0]),
],
)
def test_two_values_in_data_range(input_range: list[float]) -> None:
"""If the "data_range" field of the "Data" model contains more or less than two values, we should raise a
ValidationError.
"""
with pytest.raises(
pydantic.ValidationError,
match=f"1 validation error for Data\ndata_range\n List should have "
f"at {'least' if len(input_range) < 2 else 'most'} 2 items "
f"after validation, not {len(input_range)}",
):
ratapi.models.Data(data_range=input_range)
@pytest.mark.parametrize(
"input_range",
[
([]),
([1.0]),
([1.0, 2.0, 3.0]),
],
)
def test_two_values_in_simulation_range(input_range: list[float]) -> None:
"""If the "simulation_range" field of the "Data" model contains more or less than two values, we should raise a
ValidationError.
"""
with pytest.raises(
pydantic.ValidationError,
match=f"1 validation error for Data\nsimulation_range\n List should "
f"have at {'least' if len(input_range) < 2 else 'most'} 2 items "
f"after validation, not {len(input_range)}",
):
ratapi.models.Data(simulation_range=input_range)
@pytest.mark.parametrize(
"field",
[
"data_range",
"simulation_range",
],
)
def test_min_max_in_range(field: str) -> None:
"""If the maximum value of the "data_range" or "simulation_range" fields of the "Data" model is greater than the
minimum value, we should raise a ValidationError.
"""
with pytest.raises(
pydantic.ValidationError,
match=f"1 validation error for Data\n{field}\n Value error, {field} "
f'"min" value is greater than the "max" value',
):
ratapi.models.Data(**{field: [1.0, 0.0]})
def test_default_ranges() -> None:
"""If "data" is specified but either the "data_range" or "simulation_range" fields are not, we set the ranges to
the minimum and maximum values of the first column of the data.
"""
test_data = ratapi.models.Data(data=np.array([[1.0, 0.0, 0.0], [3.0, 0.0, 0.0]]))
assert test_data.data_range == [1.0, 3.0]
assert test_data.simulation_range == [1.0, 3.0]
@pytest.mark.parametrize(
"test_range",
[
[0.0, 2.0],
[2.0, 4.0],
[0.0, 4.0],
],
)
def test_data_range(test_range) -> None:
"""If "data" is specified but the "data_range" lies outside of the limits of the data we should raise a
ValidationError.
"""
with pytest.raises(
pydantic.ValidationError,
match=re.escape(
f"1 validation error for Data\n Value error, The "
f"data_range value of: {test_range} must lie within "
f"the min/max values of the data: [1.0, 3.0]",
),
):
ratapi.models.Data(data=np.array([[1.0, 0.0, 0.0], [3.0, 0.0, 0.0]]), data_range=test_range)
@pytest.mark.parametrize(
"test_range",
[
[0.0, 2.0],
[2.0, 4.0],
[1.5, 2.5],
],
)
def test_simulation_range(test_range) -> None:
"""If "data" is specified but the "simulation_range" lies within the limits of the data we should raise a
ValidationError.
"""
with pytest.raises(
pydantic.ValidationError,
match=re.escape(
f"1 validation error for Data\n Value error, The "
f"simulation_range value of: {test_range} must lie "
f"outside of the min/max values of the data: "
f"[1.0, 3.0]",
),
):
ratapi.models.Data(data=np.array([[1.0, 0.0, 0.0], [3.0, 0.0, 0.0]]), simulation_range=test_range)
@pytest.mark.parametrize(
["minimum", "value", "maximum"],
[
(0.0, 2.0, 1.0),
(0, 1, 0),
(1, -1, 1),
],
)
def test_parameter_range(minimum: float, value: float, maximum: float) -> None:
"""For the "Parameter" model, if the value of the "value" field does not lie with the values given in the "min" and
"max" fields, we should raise a ValidationError.
"""
with pytest.raises(
pydantic.ValidationError,
match=f"1 validation error for Parameter\n Value error, value "
f"{float(value)} is not within the defined range: "
f"{float(minimum)} <= value <= {float(maximum)}",
):
ratapi.models.Parameter(min=minimum, value=value, max=maximum)
def test_layer_bad_imaginary_SLD():
"""If 'SLD_imaginary' is given to a Layer, it should raise a descriptive ValidationError."""
with pytest.raises(
pydantic.ValidationError,
match=(
"1 validation error for Layer\n"
" Value error, The Layer class does not support imaginary SLD."
" Use the AbsorptionLayer class instead."
),
):
ratapi.models.Layer(name="My Layer", SLD_imaginary="bad sld")
def test_contrast_bad_ratio():
"""If 'domain_ratios' is given to a Contrast, it should raise a descriptive ValidationError."""
with pytest.raises(
pydantic.ValidationError,
match=(
"1 validation error for Contrast\n"
" Value error, The Contrast class does not support domain ratios."
" Use the ContrastWithRatio class instead."
),
):
ratapi.models.Contrast(name="My Contrast", domain_ratio="bad ratio")
@pytest.mark.parametrize(
["model", "type", "values"],
[
(ratapi.models.Background, "function", ["val1", "val2", "val3", "val4", "val5"]),
(ratapi.models.Resolution, "constant", ["", "", "", "", ""]),
],
)
def test_type_change_clear(model, type, values):
"""If the type of a background or resolution is changed, it should wipe the other fields and warn the user."""
model_instance = model(
name="Test",
type=type,
source="src",
value_1=values[0],
value_2=values[1],
value_3=values[2],
value_4=values[3],
value_5=values[4],
)
with pytest.warns(UserWarning, match="Changing the type of Test clears its source and value fields."):
model_instance.type = "data"
for attr in ["source", "value_1", "value_2", "value_3", "value_4", "value_5"]:
assert getattr(model_instance, attr) == ""
@pytest.mark.parametrize(
["model", "signal_type", "values"],
[
(ratapi.models.Background, "constant", ["value_1", "value_2", "value_3", "value_4", "value_5"]),
(ratapi.models.Background, "data", ["value_2", "value_3", "value_4", "value_5"]),
(ratapi.models.Resolution, "constant", ["value_1", "value_2", "value_3", "value_4", "value_5"]),
(ratapi.models.Resolution, "data", ["value_1", "value_2", "value_3", "value_4", "value_5"]),
],
)
def test_unsupported_parameters_error(model, signal_type, values):
"""If a value is inputted for an unsupported field for a particular type of background or resolution then we should
raise an error."""
for value in values:
with pytest.raises(
pydantic.ValidationError,
match=(
f"1 validation error for {model.__name__}\n Value error, The following values are not supported"
f' by the "{signal_type}" {model.__name__} type: {value}'
),
):
model(**{"type": signal_type, value: "unsupported"})
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