mirror of
https://github.com/p2p-ld/nwb-linkml.git
synced 2024-11-12 17:54:29 +00:00
remove outdated generator tests, unskip module
This commit is contained in:
parent
ae37db3a41
commit
dfeac9e808
3 changed files with 52 additions and 210 deletions
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@ -7,8 +7,6 @@ See class and module docstrings for details :)
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import re
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from dataclasses import dataclass, field
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from pathlib import Path
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from types import ModuleType
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from typing import Callable, ClassVar, Dict, List, Optional, Tuple
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from linkml.generators import PydanticGenerator
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@ -146,7 +144,6 @@ class NWBPydanticGenerator(PydanticGenerator):
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cls = AfterGenerateClass.inject_dynamictable(cls)
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cls = AfterGenerateClass.wrap_dynamictable_columns(cls, sv)
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cls = AfterGenerateClass.inject_dynamictable_imports(cls, sv, self._get_element_import)
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cls = AfterGenerateClass.strip_vector_data_slots(cls, sv)
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return cls
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def before_render_template(self, template: PydanticModule, sv: SchemaView) -> PydanticModule:
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@ -157,25 +154,6 @@ class NWBPydanticGenerator(PydanticGenerator):
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del template.meta["source_file"]
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return template
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def compile_module(
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self, module_path: Path = None, module_name: str = "test", **kwargs
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) -> ModuleType: # pragma: no cover - replaced with provider
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"""
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Compiles generated python code to a module
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:return:
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"""
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pycode = self.serialize(**kwargs)
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if module_path is not None:
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module_path = Path(module_path)
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init_file = module_path / "__init__.py"
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with open(init_file, "w") as ifile:
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ifile.write(" ")
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try:
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return compile_python(pycode, module_path, module_name)
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except NameError as e:
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raise e
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class AfterGenerateSlot:
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"""
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@ -373,15 +351,6 @@ class AfterGenerateClass:
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cls.imports += imp
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return cls
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@staticmethod
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def strip_vector_data_slots(cls: ClassResult, sv: SchemaView) -> ClassResult:
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"""
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Remove spurious ``vector_data`` slots from DynamicTables
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"""
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if "vector_data" in cls.cls.attributes:
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del cls.cls.attributes["vector_data"]
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return cls
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def wrap_preserving_optional(annotation: str, wrap: str) -> str:
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"""
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63
nwb_linkml/tests/fixtures/schema.py
vendored
63
nwb_linkml/tests/fixtures/schema.py
vendored
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@ -5,8 +5,10 @@ from typing import Dict, Optional
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import pytest
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from linkml_runtime.dumpers import yaml_dumper
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from linkml_runtime.linkml_model import (
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from linkml_runtime.linkml_model.meta import (
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ArrayExpression,
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ClassDefinition,
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DimensionExpression,
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Prefix,
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SchemaDefinition,
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SlotDefinition,
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@ -88,10 +90,6 @@ def linkml_schema_bare() -> TestSchemas:
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equals_string="toplevel",
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identifier=True,
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),
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SlotDefinition(name="array", range="MainTopLevel__Array"),
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SlotDefinition(
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name="SkippableSlot", description="A slot that was meant to be skipped!"
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),
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SlotDefinition(
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name="inline_dict",
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description=(
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@ -103,35 +101,42 @@ def linkml_schema_bare() -> TestSchemas:
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inlined_as_list=False,
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any_of=[{"range": "OtherClass"}, {"range": "StillAnotherClass"}],
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),
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],
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),
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ClassDefinition(
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name="MainTopLevel__Array",
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description="Main class's array",
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is_a="Arraylike",
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attributes=[
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SlotDefinition(name="x", range="numeric", required=True),
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SlotDefinition(name="y", range="numeric", required=True),
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SlotDefinition(
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name="z",
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name="value",
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description="Main class's array",
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range="numeric",
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required=False,
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maximum_cardinality=3,
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minimum_cardinality=3,
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),
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SlotDefinition(
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name="a",
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range="numeric",
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required=False,
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minimum_cardinality=4,
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maximum_cardinality=4,
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any_of=[
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{
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"array": ArrayExpression(
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dimensions=[
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DimensionExpression(alias="x"),
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DimensionExpression(alias="y"),
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]
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)
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},
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{
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"array": ArrayExpression(
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dimensions=[
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DimensionExpression(alias="x"),
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DimensionExpression(alias="y"),
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DimensionExpression(alias="z", exact_cardinality=3),
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]
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)
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},
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{
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"array": ArrayExpression(
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dimensions=[
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DimensionExpression(alias="x"),
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DimensionExpression(alias="y"),
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DimensionExpression(alias="z", exact_cardinality=3),
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DimensionExpression(alias="a", exact_cardinality=4),
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]
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)
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},
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],
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),
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],
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),
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ClassDefinition(
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name="skippable",
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description="A class that lives to be skipped!",
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),
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ClassDefinition(
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name="OtherClass",
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description="Another class yno!",
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@ -7,7 +7,6 @@ because it's tested in the base linkml package.
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# ruff: noqa: F821 - until the tests here settle down
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import re
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import sys
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import typing
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from types import ModuleType
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@ -16,8 +15,9 @@ from typing import Optional, TypedDict
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import numpy as np
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import pytest
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from numpydantic.ndarray import NDArrayMeta
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from pydantic import BaseModel
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from numpydantic.dtype import Float
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from linkml_runtime.utils.compile_python import compile_python
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from nwb_linkml.generators.pydantic import NWBPydanticGenerator
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from ..fixtures import (
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@ -35,7 +35,6 @@ class TestModules(TypedDict):
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TestModules.__test__ = False
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@pytest.mark.xfail()
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def generate_and_import(
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linkml_schema: TestSchemas, split: bool, generator_kwargs: Optional[dict] = None
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) -> TestModules:
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@ -45,7 +44,6 @@ def generate_and_import(
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"split": split,
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"emit_metadata": True,
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"gen_slots": True,
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"pydantic_version": "2",
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**generator_kwargs,
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}
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@ -68,20 +66,13 @@ def generate_and_import(
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sys.path.append(str(linkml_schema.core_path.parents[1]))
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core = compile_python(
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str(linkml_schema.core_path.with_suffix(".py")), module_name="test_schema.core"
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)
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imported = compile_python(
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str(linkml_schema.imported_path.with_suffix(".py")), module_name="test_schema.imported"
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)
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namespace = compile_python(
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str(linkml_schema.namespace_path.with_suffix(".py")), module_name="test_schema.namespace"
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)
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core = compile_python(str(linkml_schema.core_path.with_suffix(".py")))
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imported = compile_python(str(linkml_schema.imported_path.with_suffix(".py")))
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namespace = compile_python(str(linkml_schema.namespace_path.with_suffix(".py")))
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return TestModules(core=core, imported=imported, namespace=namespace, split=split)
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@pytest.mark.xfail()
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@pytest.fixture(scope="module", params=["split", "unsplit"])
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def imported_schema(linkml_schema, request) -> TestModules:
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"""
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@ -90,97 +81,10 @@ def imported_schema(linkml_schema, request) -> TestModules:
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"""
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split = request.param == "split"
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yield generate_and_import(linkml_schema, split)
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del sys.modules["test_schema.core"]
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del sys.modules["test_schema.imported"]
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del sys.modules["test_schema.namespace"]
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return generate_and_import(linkml_schema, split)
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def _model_correctness(modules: TestModules):
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"""
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Shared assertions for model correctness.
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Only tests very basic things like type and existence,
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more specific tests are in their own test functions!
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"""
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assert issubclass(modules["core"].MainTopLevel, BaseModel)
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assert issubclass(modules["core"].Skippable, BaseModel)
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assert issubclass(modules["core"].OtherClass, BaseModel)
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assert issubclass(modules["core"].StillAnotherClass, BaseModel)
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assert issubclass(modules["imported"].MainThing, BaseModel)
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@pytest.mark.xfail()
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def test_generate(linkml_schema):
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"""
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Base case, we can generate pydantic models from linkml schema
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Tests basic functionality of serializer including
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- serialization
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- compilation (loading as a python model)
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- existence and correctness of attributes
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"""
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modules = generate_and_import(linkml_schema, split=False)
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assert isinstance(modules["core"], ModuleType)
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assert isinstance(modules["imported"], ModuleType)
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assert isinstance(modules["namespace"], ModuleType)
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_model_correctness(modules)
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# unsplit modules should have all the classes present, even if they aren't defined in it
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assert modules["core"].MainThing.__module__ == "test_schema.core"
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assert issubclass(modules["core"].MainTopLevel, modules["core"].MainThing)
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del sys.modules["test_schema.core"]
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del sys.modules["test_schema.imported"]
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del sys.modules["test_schema.namespace"]
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@pytest.mark.xfail()
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def test_generate_split(linkml_schema):
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"""
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We can generate schema split into separate files
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"""
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modules = generate_and_import(linkml_schema, split=True)
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assert isinstance(modules["core"], ModuleType)
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assert isinstance(modules["imported"], ModuleType)
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assert isinstance(modules["namespace"], ModuleType)
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_model_correctness(modules)
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# split modules have classes defined once and imported
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assert modules["core"].MainThing.__module__ == "test_schema.imported"
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# can't assert subclass here because of the weird way relative imports work
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# when we don't actually import using normal python import machinery
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assert modules["core"].MainTopLevel.__mro__[1].__module__ == "test_schema.imported"
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del sys.modules["test_schema.core"]
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del sys.modules["test_schema.imported"]
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del sys.modules["test_schema.namespace"]
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@pytest.mark.xfail()
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def test_versions(linkml_schema):
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"""
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We can use explicit versions that import from relative paths generated by
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SchemaProvider
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"""
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# here all we do is check that we have the correct relative import, since we test
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# the actual generation of these path structures elsewhere in the provider tests
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core_str = NWBPydanticGenerator(
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str(linkml_schema.core_path), versions={"imported": "v4.2.0"}, split=True
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).serialize()
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# the import should be like
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# from ...imported.v4_2_0.imported import (
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# MainThing
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# )
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match = re.findall(r"from \.\.\.imported\.v4_2_0.*?MainThing.*?\)", core_str, flags=re.DOTALL)
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assert len(match) == 1
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@pytest.mark.xfail()
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def test_arraylike(imported_schema):
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def test_array(imported_schema):
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"""
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Arraylike classes are converted to slots that specify nptyping arrays
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@ -191,19 +95,18 @@ def test_arraylike(imported_schema):
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]] = Field(None)
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"""
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# check that we have gotten an NDArray annotation and its shape is correct
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array = imported_schema["core"].MainTopLevel.model_fields["array"].annotation
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array = imported_schema["core"].MainTopLevel.model_fields["value"].annotation
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args = typing.get_args(array)
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for i, _ in enumerate(("* x, * y", "* x, * y, 3 z", "* x, * y, 3 z, 4 a")):
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for i, shape in enumerate(("* x, * y", "* x, * y, 3 z", "* x, * y, 3 z, 4 a")):
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assert isinstance(args[i], NDArrayMeta)
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assert args[i].__args__[0].__args__
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assert args[i].__args__[1] == np.number
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assert args[i].__args__[0].__args__[0] == shape
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assert args[i].__args__[1] == Float
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# we shouldn't have an actual class for the array
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assert not hasattr(imported_schema["core"], "MainTopLevel__Array")
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assert not hasattr(imported_schema["core"], "MainTopLevelArray")
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@pytest.mark.xfail()
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def test_inject_fields(imported_schema):
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"""
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Our root model should have the special fields we injected
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@ -213,35 +116,6 @@ def test_inject_fields(imported_schema):
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assert "object_id" in base.model_fields
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@pytest.mark.xfail()
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def test_linkml_meta(imported_schema):
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"""
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We should be able to store some linkml metadata with our classes
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"""
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meta = imported_schema["core"].LinkML_Meta
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assert "tree_root" in meta.model_fields
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assert imported_schema["core"].MainTopLevel.linkml_meta.default.tree_root
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assert not imported_schema["core"].OtherClass.linkml_meta.default.tree_root
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@pytest.mark.xfail()
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def test_skip(linkml_schema):
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"""
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We can skip slots and classes
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"""
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modules = generate_and_import(
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linkml_schema,
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split=False,
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generator_kwargs={
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"SKIP_SLOTS": ("SkippableSlot",),
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"SKIP_CLASSES": ("Skippable", "skippable"),
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},
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)
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assert not hasattr(modules["core"], "Skippable")
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assert "SkippableSlot" not in modules["core"].MainTopLevel.model_fields
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@pytest.mark.xfail()
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def test_inline_with_identifier(imported_schema):
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"""
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By default, if a class has an identifier attribute, it is inlined
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@ -256,7 +130,6 @@ def test_inline_with_identifier(imported_schema):
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assert stillanother is imported_schema["core"].StillAnotherClass
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@pytest.mark.xfail()
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def test_namespace(imported_schema):
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"""
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Namespace schema import all classes from the other schema
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@ -269,23 +142,18 @@ def test_namespace(imported_schema):
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("MainThing", "test_schema.imported"),
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("Arraylike", "test_schema.imported"),
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("MainTopLevel", "test_schema.core"),
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("Skippable", "test_schema.core"),
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("OtherClass", "test_schema.core"),
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("StillAnotherClass", "test_schema.core"),
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):
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assert hasattr(ns, classname)
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if imported_schema["split"]:
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assert getattr(ns, classname).__module__ == modname
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module_end_name = ".".join(getattr(ns, classname).__module__.split(".")[-2:])
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assert module_end_name == modname
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@pytest.mark.xfail()
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def test_get_set_item(imported_schema):
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"""We can get and set without explicitly addressing array"""
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cls = imported_schema["core"].MainTopLevel(array=np.array([[1, 2, 3], [4, 5, 6]]))
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cls[0] = 50
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assert (cls[0] == 50).all()
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assert (cls.array[0] == 50).all()
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def test_get_item(imported_schema):
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"""We can get without explicitly addressing array"""
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cls = imported_schema["core"].MainTopLevel(value=np.array([[1, 2, 3], [4, 5, 6]], dtype=float))
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assert np.array_equal(cls[0], np.array([1, 2, 3], dtype=float))
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cls[1, 1] = 100
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assert cls[1, 1] == 100
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assert cls.array[1, 1] == 100
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