mirror of
https://github.com/p2p-ld/nwb-linkml.git
synced 2024-11-14 02:34:28 +00:00
297 lines
10 KiB
Python
297 lines
10 KiB
Python
import pytest
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import os
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from typing import NamedTuple, Optional, List, Dict
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from dataclasses import dataclass, field
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from linkml_runtime.dumpers import yaml_dumper
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from nwb_linkml.io import schema as io
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from nwb_linkml.adapters.namespaces import NamespacesAdapter
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from nwb_schema_language import Schema, Group, Dataset, Attribute
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from linkml_runtime.linkml_model import SchemaDefinition, ClassDefinition, SlotDefinition, Prefix, TypeDefinition
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import shutil
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from pathlib import Path
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@pytest.fixture(scope="session")
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def tmp_output_dir() -> Path:
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path = Path(__file__).parent.resolve() / '__tmp__'
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if path.exists():
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shutil.rmtree(str(path))
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path.mkdir()
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return path
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@pytest.fixture(scope="function")
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def tmp_output_dir_func(tmp_output_dir) -> Path:
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"""
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tmp output dir that gets cleared between every function
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cleans at the start rather than at cleanup in case the output is to be inspected
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"""
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subpath = tmp_output_dir / '__tmpfunc__'
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if subpath.exists():
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shutil.rmtree(str(subpath))
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subpath.mkdir()
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return subpath
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@pytest.fixture(scope="module")
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def tmp_output_dir_mod(tmp_output_dir) -> Path:
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"""
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tmp output dir that gets cleared between every function
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cleans at the start rather than at cleanup in case the output is to be inspected
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"""
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subpath = tmp_output_dir / '__tmpmod__'
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if subpath.exists():
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shutil.rmtree(str(subpath))
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subpath.mkdir()
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return subpath
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@pytest.fixture(autouse=True, scope='session')
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def set_config_vars(tmp_output_dir):
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os.environ['NWB_LINKML_CACHE_DIR'] = str(tmp_output_dir)
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@pytest.fixture(
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scope="session",
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params=[
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{'core_version': "2.6.0", 'hdmf_version': '1.5.0'}
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])
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def nwb_core_fixture(request) -> NamespacesAdapter:
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nwb_core = io.load_nwb_core(**request.param)
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nwb_core.populate_imports()
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return nwb_core
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@pytest.fixture(scope="session")
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def data_dir() -> Path:
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path = Path(__file__).parent.resolve() / 'data'
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return path
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@dataclass
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class TestSchemas():
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__test__ = False
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core: SchemaDefinition
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imported: SchemaDefinition
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namespace: SchemaDefinition
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core_path: Optional[Path] = None
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imported_path: Optional[Path] = None
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namespace_path: Optional[Path] = None
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@pytest.fixture(scope="module")
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def linkml_schema_bare() -> TestSchemas:
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schema = TestSchemas(
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core=SchemaDefinition(
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name="core",
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id="core",
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version="1.0.1",
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imports=["imported",'linkml:types'],
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default_prefix="core",
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prefixes={'linkml': Prefix('linkml','https://w3id.org/linkml')},
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description="Test core schema",
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classes=[
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ClassDefinition(
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name="MainTopLevel",
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description="The main class we are testing!",
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is_a="MainThing",
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tree_root=True,
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attributes=[
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SlotDefinition(
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name="name",
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description="A fixed property that should use Literal and be frozen",
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range="string",
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required=True,
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ifabsent="string(toplevel)",
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equals_string="toplevel",
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identifier=True
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),
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SlotDefinition(
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name="array",
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range="MainTopLevel__Array"
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),
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SlotDefinition(
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name="SkippableSlot",
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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="This should be inlined as a dictionary despite this class having an identifier",
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multivalued=True,
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inlined=True,
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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(
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name="x",
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range="numeric",
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required=True
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),
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SlotDefinition(
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name="y",
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range="numeric",
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required=True
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),
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SlotDefinition(
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name="z",
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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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)
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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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attributes=[
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SlotDefinition(
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name="name",
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range="string",
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required=True,
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identifier=True
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)
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]
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),
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ClassDefinition(
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name="StillAnotherClass",
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description="And yet another!",
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attributes=[
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SlotDefinition(
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name="name",
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range="string",
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required=True,
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identifier=True
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)
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]
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)
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],
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types=[
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TypeDefinition(
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name="numeric",
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typeof="float"
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)
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]
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),
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imported=SchemaDefinition(
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name="imported",
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id="imported",
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version="1.4.5",
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default_prefix="core",
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imports=['linkml:types'],
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prefixes = {'linkml': Prefix('linkml', 'https://w3id.org/linkml')},
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classes = [
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ClassDefinition(
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name="MainThing",
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description="Class imported by our main thing class!",
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attributes=[
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SlotDefinition(
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name="meta_slot",
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range="string"
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)
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]
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),
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ClassDefinition(
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name="Arraylike",
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abstract=True
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)
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]
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),
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namespace=SchemaDefinition(
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name="namespace",
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id="namespace",
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version="1.1.1",
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default_prefix="namespace",
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annotations={'namespace': {'tag': 'namespace', 'value': 'True'}},
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description="A namespace package that should import all other classes",
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imports=['core', 'imported']
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)
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)
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return schema
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@pytest.fixture(scope="module")
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def linkml_schema(tmp_output_dir_mod, linkml_schema_bare) -> TestSchemas:
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"""
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A test schema that includes
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- Two schemas, one importing from the other
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- Arraylike
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- Required/static "name" field
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- linkml metadata like tree_root
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- skipping classes
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"""
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schema = linkml_schema_bare
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test_schema_path = tmp_output_dir_mod / 'test_schema'
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test_schema_path.mkdir()
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core_path = test_schema_path / 'core.yaml'
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imported_path = test_schema_path / 'imported.yaml'
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namespace_path = test_schema_path / 'namespace.yaml'
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schema.core_path = core_path
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schema.imported_path = imported_path
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schema.namespace_path = namespace_path
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yaml_dumper.dump(schema.core, schema.core_path)
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yaml_dumper.dump(schema.imported, schema.imported_path)
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yaml_dumper.dump(schema.namespace, schema.namespace_path)
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return schema
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@dataclass
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class NWBSchemaTest():
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datasets: Dict[str, Dataset] = field(default_factory=dict)
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groups: Dict[str, Group] = field(default_factory=dict)
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@pytest.fixture()
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def nwb_schema() -> NWBSchemaTest:
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"""Minimal NWB schema for testing"""
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image = Dataset(
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neurodata_type_def="Image",
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dtype="numeric",
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neurodata_type_inc="NWBData",
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dims=[['x', 'y'], ['x', 'y', 'r, g, b'], ['x', 'y', 'r, g, b, a']],
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shape=[[None, None], [None, None, 3], [None, None, 4]],
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doc="An image!",
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attributes = [
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Attribute(dtype="float32", name="resolution", doc="resolution!"),
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Attribute(dtype="text", name="description", doc='Description!')
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]
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)
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images = Group(
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neurodata_type_def='Images',
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neurodata_type_inc='NWBDataInterface',
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default_name="Images",
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doc='Images!',
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attributes=[
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Attribute(dtype="text", name='description', doc="description!")
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],
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datasets=[
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Dataset(neurodata_type_inc='Image', quantity="+", doc="images!"),
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Dataset(neurodata_type_inc='ImageReferences',
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name='order_of_images',
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doc="Image references!",
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quantity='?'
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)
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]
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)
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return NWBSchemaTest(datasets=[image], groups=[images])
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