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test_types.py
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460 lines (432 loc) · 13.4 KB
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import pytest
from openfoodfacts.types import (
Flavor,
JSONType,
NutritionV3,
NutritionV3InputSet,
NutritionV3NutrientInput,
TaxonomyType,
)
class TestFlavor:
def test_str(self):
assert str(Flavor.off) == "off"
def test_from_product_type_food(self):
assert Flavor.from_product_type("food") == Flavor.off
def test_from_product_type_beauty(self):
assert Flavor.from_product_type("beauty") == Flavor.obf
def test_from_product_type_petfood(self):
assert Flavor.from_product_type("petfood") == Flavor.opff
def test_from_product_type_product(self):
assert Flavor.from_product_type("product") == Flavor.opf
def test_from_product_type_invalid(self):
with pytest.raises(
ValueError, match="no Flavor matched with product_type 'invalid'"
):
Flavor.from_product_type("invalid")
class TestTaxonomyType:
def test_str(self):
assert str(TaxonomyType.category) == "category"
def test_type_unknown(self):
with pytest.raises(AttributeError):
_ = TaxonomyType.unknown
def test_dataset_path_known(self):
assert (
TaxonomyType.category.dataset_path == "data/taxonomies/categories.full.json"
)
def test_dataset_path_unknown(self):
with pytest.raises(AttributeError):
_ = TaxonomyType.unknown.dataset_path
NUTRITION_1 = {
"aggregated_set": {
"nutrients": {
"salt": {
"source": "packaging",
"source_index": 0,
"source_per": "serving",
"unit": "g",
"value": 0.5,
},
"saturated-fat": {
"source": "packaging",
"source_index": 0,
"source_per": "serving",
"unit": "g",
"value": 0.5,
},
},
"per": "100g",
"preparation": "as_sold",
},
"input_sets": [
{
"nutrients": {
"salt": {"unit": "g", "value": 1, "value_string": "1"},
"saturated-fat": {
"unit": "g",
"value": 1,
"value_string": "1",
},
},
"per": "serving",
"per_quantity": 200,
"per_unit": "g",
"preparation": "as_sold",
"source": "packaging",
}
],
}
NUTRITION_2 = {
"aggregated_set": {
"nutrients": {
"salt": {
"modifier": "<=",
"source": "packaging",
"source_index": 0,
"source_per": "100g",
"unit": "g",
"value": 5,
},
"sodium": {
"modifier": "<=",
"source": "packaging",
"source_index": 0,
"source_per": "100g",
"unit": "g",
"value": 2,
},
"sugars": {
"source": "usda",
"source_index": 1,
"source_per": "100g",
"unit": "g",
"value": 5.2,
},
},
"per": "100g",
"preparation": "as_sold",
},
"input_sets": [
{
"nutrients": {
"sodium": {
"modifier": "<=",
"unit": "g",
"value": 2,
"value_string": "2.0",
}
},
"per": "100g",
"per_quantity": "100",
"per_unit": "g",
"preparation": "as_sold",
"source": "packaging",
},
{
"nutrients": {
"sodium": {
"unit": "g",
"value": 0.1,
"value_string": "0.1",
},
"sugars": {
"unit": "g",
"value": 5.2,
"value_string": "5.2",
},
},
"per": "100g",
"per_quantity": "100",
"per_unit": "g",
"preparation": "as_sold",
"source": "usda",
},
],
}
# With `value_computed` in the input_set
NUTRITION_3 = {
"aggregated_set": {
"nutrients": {
"carbohydrates": {
"modifier": "<",
"source": "packaging",
"source_index": 0,
"source_per": "100g",
"unit": "g",
"value": 0.5,
},
"energy": {
"source": "packaging",
"source_index": 0,
"source_per": "100g",
"unit": "kJ",
"value": 332,
"value_computed": 340.4,
},
"energy-kcal": {
"source": "packaging",
"source_index": 0,
"source_per": "100g",
"unit": "kcal",
"value": 78,
"value_computed": 80.3,
},
"energy-kj": {
"source": "packaging",
"source_index": 0,
"source_per": "100g",
"unit": "kJ",
"value": 332,
"value_computed": 340.4,
},
"fat": {
"source": "packaging",
"source_index": 0,
"source_per": "100g",
"unit": "g",
"value": 0.7,
},
"proteins": {
"source": "packaging",
"source_index": 0,
"source_per": "100g",
"unit": "g",
"value": 18,
},
"salt": {
"source": "packaging",
"source_index": 0,
"source_per": "100g",
"unit": "g",
"value": 1.2,
},
"saturated-fat": {
"modifier": "<",
"source": "packaging",
"source_index": 0,
"source_per": "100g",
"unit": "g",
"value": 0.1,
},
"sodium": {
"modifier": "~",
"source": "packaging",
"source_index": 0,
"source_per": "100g",
"unit": "g",
"value": 0.48,
},
"sugars": {
"modifier": "<",
"source": "packaging",
"source_index": 0,
"source_per": "100g",
"unit": "g",
"value": 0.5,
},
},
"per": "100g",
"preparation": "as_sold",
},
"input_sets": [
{
"nutrients": {
"carbohydrates": {
"modifier": "<",
"unit": "g",
"value": 0.5,
"value_string": "0.5",
},
"energy-kcal": {
"unit": "kcal",
"value": 78,
"value_computed": 80.3,
"value_string": "78",
},
"energy-kj": {
"unit": "kJ",
"value": 332,
"value_computed": 340.4,
"value_string": "332",
},
"fat": {"unit": "g", "value": 0.7, "value_string": "0.7"},
"proteins": {"unit": "g", "value": 18, "value_string": "18"},
"salt": {"unit": "g", "value": 1.2, "value_string": "1.2"},
"saturated-fat": {
"modifier": "<",
"unit": "g",
"value": 0.1,
"value_string": "0.1",
},
"sodium": {"unit": "g", "value_computed": 0.48},
"sugars": {
"modifier": "<",
"unit": "g",
"value": 0.5,
"value_string": "0.5",
},
},
"per": "100g",
"per_quantity": 100,
"per_unit": "g",
"preparation": "as_sold",
"source": "packaging",
"unspecified_nutrients": ["fiber"],
}
],
}
# With missing er_quantity and per_unit in the input_set
NUTRITION_4 = {
"input_sets": [
{
"last_updated_t": 1771468068,
"per_unit": "g",
"source": "packaging",
"per": "100g",
"per_quantity": 100,
"nutrients": {"nova-group": {"unit": "", "value": 4, "value_string": "4"}},
"preparation": "as_sold",
},
{
"per_unit": "g",
"source_description": "Estimate from ingredients",
"preparation": "as_sold",
"nutrients": {
"fruits-vegetables-legumes": {
"value_string": "0",
"unit": "%",
"value": 0,
"modifier": "~",
},
"added-sugars": {
"value": 0.182291666666664,
"modifier": "~",
"unit": "g",
"value_string": "0.182291666666664",
},
"fruits-vegetables-nuts": {
"value_string": "0",
"value": 0,
"modifier": "~",
"unit": "%",
},
},
"per_quantity": 100,
"per": "100g",
"source": "estimate",
},
{
"source": "packaging",
"per": "serving",
"preparation": "as_sold",
"nutrients": {
"energy-kj": {"unit": "kJ", "value_computed": 0},
"carbohydrates": {"value_string": "0", "unit": "g", "value": 0},
"energy-kcal": {
"value_string": "0",
"value": 0,
"value_computed": 0,
"unit": "kcal",
},
"fat": {"value_string": "0", "unit": "g", "value": 0},
"proteins": {"value_string": "0", "value": 0, "unit": "g"},
},
},
],
"aggregated_set": {
"per": "100g",
"preparation": "as_sold",
"nutrients": {
"fruits-vegetables-legumes": {
"source_per": "100g",
"source_index": 1,
"value": 0,
"modifier": "~",
"unit": "%",
"source": "estimate",
},
"added-sugars": {
"unit": "g",
"source": "estimate",
"modifier": "~",
"value": 0.182291666666664,
"source_index": 1,
"source_per": "100g",
},
"nova-group": {
"value": 4,
"source": "packaging",
"unit": "",
"source_per": "100g",
"source_index": 0,
},
"fruits-vegetables-nuts": {
"modifier": "~",
"value": 0,
"source": "estimate",
"unit": "%",
"source_per": "100g",
"source_index": 1,
},
},
},
}
class TestNutritionV3:
@pytest.mark.parametrize(
"obj", [NUTRITION_1, NUTRITION_2, NUTRITION_3, NUTRITION_4]
)
def test_parse(self, obj: JSONType):
NutritionV3.model_validate(obj)
def test_filter_input_sets(self):
nutrition_obj = NutritionV3.model_validate(NUTRITION_2)
results = nutrition_obj.filter_input_sets(source="packaging")
assert len(results) == 1
assert isinstance(results[0], NutritionV3InputSet)
assert (
len(nutrition_obj.filter_input_sets(source="packaging", per="100ml")) == 0
)
def test_get_input_nutrient(self):
nutrition_obj = NutritionV3.model_validate(NUTRITION_2)
result = nutrition_obj.get_input_nutrient(
nutrient="sodium",
per="100g",
preparation="as_sold",
per_quantity=100,
source="packaging",
per_unit="g",
)
assert result is not None
assert result.value == 2
assert result.value_string == "2.0"
assert result.unit == "g"
assert result.modifier == "<="
# We expect no result here
result = nutrition_obj.get_input_nutrient(
nutrient="sodium",
# per was modified here
per="serving",
preparation="as_sold",
per_quantity=100,
source="packaging",
per_unit="g",
)
assert result is None
result = nutrition_obj.get_input_nutrient(
nutrient="sugars",
source="usda",
)
assert result is not None
assert result.value == 5.2
assert result.value_string == "5.2"
assert result.unit == "g"
assert result.modifier is None
class TestNutritionV3NutrientInput:
def test_ensure_value_string_is_not_an_int_or_float(self):
parsed = NutritionV3NutrientInput.model_validate(
{
"unit": "g",
"value": 5.2,
"value_string": 5.2,
"modifier": "<",
}
)
assert parsed.value_string == "5.2"