From 40984a65823a285ebf8e557865b24297de9a2eb6 Mon Sep 17 00:00:00 2001 From: sneakers-the-rat Date: Fri, 22 Sep 2023 00:31:34 -0700 Subject: [PATCH] figuring out the strategy here... - added linkml_meta classvar to store additional linkml properties if needed - injecting path field to metaclass - sketch of doing a queue-based read - prune datasets & example allen institute data --- docs/conf.py | 3 +- nwb_linkml/poetry.lock | 374 +++++++++--------- nwb_linkml/pyproject.toml | 10 +- .../src/nwb_linkml/generators/pydantic.py | 61 ++- nwb_linkml/src/nwb_linkml/io/hdf5.py | 373 ++++++++++------- nwb_linkml/src/nwb_linkml/providers/schema.py | 46 ++- nwb_linkml/tests/data/aibs_ecephys.nwb | Bin 0 -> 2853335 bytes nwb_linkml/tests/data/sources.txt | 4 + nwb_linkml/tests/test_io/test_io_hdf5.py | 73 ++++ .../test_providers/test_provider_schema.py | 18 +- 10 files changed, 607 insertions(+), 355 deletions(-) create mode 100644 nwb_linkml/tests/data/aibs_ecephys.nwb create mode 100644 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"jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (<7.2.5)", "sphinx (>=3.5)", "sphinx-lint"] testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy (>=0.9.1)", "pytest-ruff"] [extras] plot = ["dash", "dash-cytoscape"] -tests = ["coverage", "coveralls", "pytest", "pytest-cov", "pytest-depends", "pytest-emoji", "pytest-md", "pytest-profiling"] +tests = ["coverage", "coveralls", "pytest", "pytest-cov", "pytest-depends", "pytest-md", "pytest-profiling"] [metadata] lock-version = "2.0" python-versions = "^3.11" -content-hash = "9ef89b731746d07d428c6cff4a8c8b4771fbfcfcc8f17120ce3c6089e5161eb6" +content-hash = "7a4e1c3b66143e4f4e8392238051241f25274ebd597183ef64168055949074f4" diff --git a/nwb_linkml/pyproject.toml b/nwb_linkml/pyproject.toml index 77ac0db..f43701b 100644 --- a/nwb_linkml/pyproject.toml +++ b/nwb_linkml/pyproject.toml @@ -65,11 +65,11 @@ build-backend = "poetry.core.masonry.api" [tool.pytest.ini_options] -addopts = [ - "--cov=nwb_linkml", - "--cov-append", - "--cov-config=.coveragerc" -] +#addopts = [ +# "--cov=nwb_linkml", +# "--cov-append", +# "--cov-config=.coveragerc" +#] testpaths = [ "tests", 'nwb_linkml/tests' diff --git a/nwb_linkml/src/nwb_linkml/generators/pydantic.py b/nwb_linkml/src/nwb_linkml/generators/pydantic.py index 3dc2ebb..f66ee7f 100644 --- a/nwb_linkml/src/nwb_linkml/generators/pydantic.py +++ b/nwb_linkml/src/nwb_linkml/generators/pydantic.py @@ -19,11 +19,12 @@ The `serialize` method import pdb from dataclasses import dataclass, field from pathlib import Path -from typing import List, Dict, Set, Tuple, Optional, TypedDict +from typing import List, Dict, Set, Tuple, Optional, TypedDict, Type import os, sys from types import ModuleType from copy import deepcopy, copy import warnings +import inspect from nwb_linkml.maps import flat_to_npytyping from linkml.generators import PydanticGenerator @@ -48,10 +49,14 @@ from linkml.utils.ifabsent_functions import ifabsent_value_declaration from nwb_linkml.maps.naming import module_case, version_module_case from jinja2 import Template +from pydantic import BaseModel + +class LinkML_Meta(BaseModel): + """Extra LinkML Metadata stored as a class attribute""" + tree_root: bool = False - -def default_template(pydantic_ver: str = "1") -> str: +def default_template(pydantic_ver: str = "1", extra_classes:Optional[List[Type[BaseModel]]] = None) -> str: """Constructs a default template for pydantic classes based on the version of pydantic""" ### HEADER ### template = """ @@ -94,7 +99,6 @@ class ConfiguredBaseModel(WeakRefShimBaseModel, extra = {% if allow_extra %}'allow'{% else %}'forbid'{% endif %}, arbitrary_types_allowed = True, use_enum_values = True): - pass """ else: template += """ @@ -104,8 +108,22 @@ class ConfiguredBaseModel(BaseModel, extra = {% if allow_extra %}'allow'{% else %}'forbid'{% endif %}, arbitrary_types_allowed = True, use_enum_values = True): - pass """ + ### Injected Fields + template += """ +{%- if injected_fields != None -%} + {% for field in injected_fields %} + {{ field }} + {% endfor %} +{%- else -%} + pass +{%- endif -%} + """ + ### Extra classes + if extra_classes is not None: + template += """{{ '\n\n' }}""" + for cls in extra_classes: + template += inspect.getsource(cls) + '\n\n' ### ENUMS ### template += """ {% for e in enums.values() %} @@ -142,11 +160,15 @@ class {{ c.name }} \"\"\" {%- endif %} {% for attr in c.attributes.values() if c.attributes -%} - {{attr.name}}: {%- if attr.equals_string -%} + {{attr.name}}:{{ ' ' }}{%- if attr.equals_string -%} Literal[{{ predefined_slot_values[c.name][attr.name] }}] {%- else -%} {{ attr.annotations['python_range'].value }} - {%- endif -%} = Field( + {%- endif -%} + {%- if attr.annotations['fixed_field'] -%} + {{ ' ' }}= {{ attr.annotations['fixed_field'].value }} + {%- else -%} + {{ ' ' }}= Field( {%- if predefined_slot_values[c.name][attr.name] -%} {{ predefined_slot_values[c.name][attr.name] }} {%- elif attr.required -%} @@ -159,6 +181,7 @@ class {{ c.name }} {%- if attr.minimum_value != None %}, ge={{attr.minimum_value}}{% endif -%} {%- if attr.maximum_value != None %}, le={{attr.maximum_value}}{% endif -%} ) + {%- endif %} {% else -%} None {% endfor %} @@ -192,6 +215,9 @@ class NWBPydanticGenerator(PydanticGenerator): # SKIP_SLOTS=('VectorData',) SKIP_SLOTS=('',) SKIP_CLASSES=('',) + INJECTED_FIELDS = ( + 'hdf5_path: Optional[str] = Field(None, description="The absolute path that this object is stored in an NWB file")', + ) # SKIP_CLASSES=('VectorData','VectorIndex') split:bool=True schema_map:Optional[Dict[str, SchemaDefinition]]=None @@ -410,6 +436,18 @@ class NWBPydanticGenerator(PydanticGenerator): union += '\n' + ' '*4 + ']' return union + def _get_linkml_classvar(self, cls:ClassDefinition) -> SlotDefinition: + """A class variable that holds additional linkml attrs""" + slot = SlotDefinition( + name='linkml_meta' + ) + slot.annotations['python_range'] = Annotation('python_range', 'ClassVar[LinkML_Meta]') + meta_fields = {k: getattr(cls, k, None) for k in LinkML_Meta.model_fields.keys()} + meta_field_strings = [f'{k}={v}' for k,v in meta_fields.items() if v is not None] + meta_field_string = ', '.join(meta_field_strings) + slot.annotations['fixed_field'] = Annotation('fixed_field', f'Field(LinkML_Meta({meta_field_string}), frozen=True)') + + return slot def sort_classes(self, clist: List[ClassDefinition], imports:Dict[str, List[str]]) -> List[ClassDefinition]: """ @@ -538,7 +576,8 @@ class NWBPydanticGenerator(PydanticGenerator): with open(self.template_file) as template_file: template_obj = Template(template_file.read()) else: - template_obj = Template(default_template(self.pydantic_version)) + template_obj = Template(default_template(self.pydantic_version, + extra_classes=[LinkML_Meta])) sv: SchemaView sv = self.schemaview @@ -581,6 +620,9 @@ class NWBPydanticGenerator(PydanticGenerator): for attribute in list(class_def.attributes.keys()): del class_def.attributes[attribute] + # make class attr that stores extra linkml attrs + class_def.attributes['linkml_meta'] = self._get_linkml_classvar(class_def) + class_name = class_original.name predefined_slot_values[camelcase(class_name)] = {} for s in class_slots[class_name]: @@ -632,6 +674,7 @@ class NWBPydanticGenerator(PydanticGenerator): pyrange = f"Optional[{pyrange}]" ann = Annotation("python_range", pyrange) s.annotations[ann.tag] = ann + code = template_obj.render( imports=imports, schema=pyschema, @@ -642,6 +685,7 @@ class NWBPydanticGenerator(PydanticGenerator): metamodel_version=self.schema.metamodel_version, version=self.schema.version, class_isa_plus_mixins=self.get_class_isa_plus_mixins(sorted_classes), + injected_fields=self.INJECTED_FIELDS ) return code @@ -678,3 +722,4 @@ def compile_python(text_or_fn: str, package_path: Path = None) -> ModuleType: # exec(spec, module.__dict__) return module + diff --git a/nwb_linkml/src/nwb_linkml/io/hdf5.py b/nwb_linkml/src/nwb_linkml/io/hdf5.py index 8f77f8e..96be760 100644 --- a/nwb_linkml/src/nwb_linkml/io/hdf5.py +++ b/nwb_linkml/src/nwb_linkml/io/hdf5.py @@ -1,154 +1,79 @@ """ This is a sandbox file that should be split out to its own pydantic-hdf5 package, but just experimenting here to get our bearings + +Notes: + + * Rather than a set of recursive build steps as is used elsewhere in the package, + since we need to instantiate some models first that are referred to elsewhere, we + flatten the hdf5 file and build each from a queue. + +Mapping operations (mostly TODO atm) + +* Create new models from DynamicTables +* Handle softlinks as object references and vice versa by adding a ``path`` attr + + + +Other TODO: + +* Read metadata only, don't read all arrays +* Write, obvi lol. + """ import pdb import typing +import warnings from typing import Optional, List, Dict, overload, Literal, Type, Any from pathlib import Path from types import ModuleType -from typing import TypeVar, TYPE_CHECKING +from typing import TypeVar, TYPE_CHECKING, NamedTuple from abc import abstractmethod import json +import subprocess +import shutil import h5py -from pydantic import BaseModel +from pydantic import BaseModel, Field, ConfigDict from dataclasses import dataclass, field from nwb_linkml.translate import generate_from_nwbfile #from nwb_linkml.models.core_nwb_file import NWBFile if TYPE_CHECKING: - from nwb_linkml.models.core_nwb_file import NWBFile + from nwb_linkml.models import NWBFile from nwb_linkml.providers.schema import SchemaProvider -@dataclass -class HDF5Element(): - cls: h5py.Dataset | h5py.Group - parent: Type[BaseModel] - model: Optional[Any] = None - root_model: Optional[Type[BaseModel]] = None - @abstractmethod - def read(self) -> BaseModel | List[BaseModel]: - """ - Constructs the pydantic model from the given hdf5 element - """ +class H5SourceItem(BaseModel): + """Tuple of items for each element when flattening an hdf5 file""" + path: str + """Absolute hdf5 path of element""" + leaf: bool + """If ``True``, this item has no children (and thus we should start instantiating it before ascending to parent classes)""" + h5_type: Literal['group', 'dataset'] + """What kind of hdf5 element this is""" + depends: List[str] = Field(default_factory=list) + """Paths of other source items that this item depends on before it can be instantiated. eg. from softlinks""" - @abstractmethod - def write(self) -> h5py.Dataset | h5py.Group: - """ - Create the h5py object from the in-memory pydantic model - """ + + + model_config = ConfigDict(arbitrary_types_allowed=True) @property - def name(self) -> str: - """Just the terminal group name""" - return self.cls.name.split('/')[-1] - - def get_model(self) -> Type[BaseModel | dict | list]: - """ - Find our model - - If we have a neurodata_type in our attrs, use that - - Otherwise, use our parent to resolve the type - """ - if self.model is not None: - return self.model - - if 'neurodata_type' in self.cls.attrs.keys(): - return get_model(self.cls) - else: - parent_model = get_model(self.cls.parent) - field = parent_model.model_fields.get(self.name) - if issubclass(type(field.annotation), BaseModel): - return field.annotation - else: - try: - if issubclass(field.annotation, BaseModel): - return field.annotation - except TypeError: - pass - # remove any optionals - annotation = field.annotation - annotation = unwrap_optional(annotation) - - if typing.get_origin(annotation) is list: - return list - - else: - return dict - #raise NotImplementedError('Need to unpack at least listlike annotations') - -def unwrap_optional(annotation): - if typing.get_origin(annotation) == typing.Union: - args = typing.get_args(annotation) - - if len(args) == 2 and args[1].__name__ == 'NoneType': - annotation = args[0] - return annotation - -def take_outer_type(annotation): - if typing.get_origin(annotation) is list: - return list - return annotation - -def submodel_by_path(model: BaseModel, path:str) -> Type[BaseModel | dict | list]: - """ - Given a pydantic model and an absolute HDF5 path, get the type annotation - """ - - -@dataclass -class H5Dataset(HDF5Element): - cls: h5py.Dataset - - def read(self) -> Any: - model = self.get_model() - - # TODO: Handle references - if self.cls.dtype == h5py.ref_dtype: - return None - - if self.cls.shape == (): - return self.cls[()] - elif model is list: - return self.cls[:].tolist() - else: - return {'array':self.cls[:], 'name': self.cls.name.split('/')[-1]} - #raise NotImplementedError('oop') - -@dataclass -class H5Group(HDF5Element): - cls: h5py.Group - - def read(self) -> BaseModel: - data = {} - model = self.get_model() - - model_attrs = { - k:v for k, v in self.cls.attrs.items() if k in model.model_fields.keys() - } - data.update(model_attrs) - - - for k, v in self.cls.items(): - child_model = None - if isinstance(model, type) and issubclass(model, BaseModel): - child_field = model.model_fields.get(k, None) - if child_field is not None: - child_model = unwrap_optional(child_field.annotation) - child_model = take_outer_type(child_model) - if isinstance(v, h5py.Group): - data[k] = H5Group(cls=v, parent=model, model=child_model).read() - elif isinstance(v, h5py.Dataset): - data[k] = H5Dataset(cls=v, parent=model, model=child_model).read() - - - if issubclass(model, BaseModel): - data['name'] = self.cls.name.split('/')[-1] - return model(**data) - elif model is list: - return list(data.values()) + def parts(self) -> List[str]: + """path split by /""" + return self.path.split('/') +class ReadQueue(BaseModel): + """Container model to store items as they are built """ + queue: Dict[str,H5SourceItem] = Field( + default_factory=dict, + description="Items left to be instantiated, keyed by hdf5 path", + ) + completed: Dict[str, BaseModel] = Field( + default_factory=dict, + description="Items that have already been instantiated, keyed by hdf5 path" + ) class HDF5IO(): @@ -169,19 +94,23 @@ class HDF5IO(): def read(self, path:str) -> BaseModel | Dict[str, BaseModel]: ... def read(self, path:Optional[str] = None): + provider = self.make_provider() + h5f = h5py.File(str(self.path)) - schema = read_specs(h5f.get('specifications')) - # build schema so we have them cached - - provider = SchemaProvider() - res = provider.build_from_dicts(schema) - if path: src = h5f.get(path) - parent = get_model(src) else: src = h5f - parent = provider.get_class('core', 'NWBFile') + + # get all children of selected item + if isinstance(src, (h5py.File, h5py.Group)): + children = self._flatten_hdf(src) + else: + raise NotImplementedError('directly read individual datasets') + + + + data = {} for k, v in src.items(): @@ -199,6 +128,38 @@ class HDF5IO(): + + + def make_provider(self) -> SchemaProvider: + """ + Create a :class:`~.providers.schema.SchemaProvider` by + reading specifications from the NWBFile ``/specification`` group and translating + them to LinkML and generating pydantic models + + Returns: + :class:`~.providers.schema.SchemaProvider` : Schema Provider with correct versions + specified as defaults + """ + h5f = h5py.File(str(self.path)) + schema = read_specs_as_dicts(h5f.get('specifications')) + + # get versions for each namespace + versions = {} + for ns_schema in schema.values(): + # each "namespace" can actually contain multiple namespaces which actually contain the version info + for inner_ns in ns_schema['namespace']['namespaces']: + versions[inner_ns['name']] = inner_ns['version'] + + provider = SchemaProvider(versions=versions) + + # build schema so we have them cached + provider.build_from_dicts(schema) + h5f.close() + return provider + + + + def process_group(self, group:h5py.Group|h5py.File) -> dict | list: attrs = dict(group.attrs) @@ -233,7 +194,17 @@ class HDF5IO(): -def read_specs(group: h5py.Group) -> dict: +def read_specs_as_dicts(group: h5py.Group) -> dict: + """ + Utility function to iterate through the `/specifications` group and + load + + Args: + group: + + Returns: + + """ spec_dict = {} def _read_spec(name, node): @@ -263,4 +234,136 @@ def get_model(cls: h5py.Group | h5py.Dataset) -> Type[BaseModel]: return mod.model_fields[cls.name.split('/')[-1]].annotation +def truncate_file(source: Path, target: Optional[Path] = None, n:int=10) -> Path: + """ + Create a truncated HDF5 file where only the first few samples are kept. + + Used primarily to create testing data from real data without it being so damn bit + + Args: + source (:class:`pathlib.Path`): Source hdf5 file + target (:class:`pathlib.Path`): Optional - target hdf5 file to write to. If ``None``, use ``{source}_truncated.hdf5`` + n (int): The number of items from datasets (samples along the 0th dimension of a dataset) to include + + Returns: + :class:`pathlib.Path` path of the truncated file + """ + if target is None: + target = source.parent / (source.stem + '_truncated.hdf5') + else: + target = Path(target) + + source = Path(source) + + # and also a temporary file that we'll make with h5repack + target_tmp = target.parent / (target.stem + '_tmp.hdf5') + + + # copy the whole thing + if target.exists(): + target.unlink() + shutil.copy(source, target) + + h5f_target = h5py.File(str(target), 'r+') + def _prune_dataset(name:str, obj: h5py.Dataset | h5py.Group): + + if isinstance(obj, h5py.Dataset): + if obj.size > 10: + try: + obj.resize(n, axis=0) + except TypeError: + # contiguous arrays cant be resized directly + # so we have to jank our way through it + tmp_name = obj.name + '__tmp' + original_name = obj.name + obj.parent.move(obj.name, tmp_name) + old_obj = obj.parent.get(tmp_name) + new_obj = obj.parent.create_dataset(original_name, data=old_obj[0:n]) + for k, v in old_obj.attrs.items(): + new_obj.attrs[k] = v + del new_obj.parent[tmp_name] + + + h5f_target.visititems(_prune_dataset) + h5f_target.flush() + h5f_target.close() + + # use h5repack to actually remove the items from the dataset + if shutil.which('h5repack') is None: + warnings.warn('Truncated file made, but since h5repack not found in path, file wont be any smaller') + return target + + res = subprocess.run( + ['h5repack', '-f', 'GZIP=9', str(target), str(target_tmp)], + capture_output=True + ) + if res.returncode != 0: + warnings.warn(f'h5repack did not return 0: {res.stderr} {res.stdout}') + # remove the attempt at the repack + target_tmp.unlink() + return target + + target.unlink() + target_tmp.rename(target) + + return target + +def unwrap_optional(annotation): + if typing.get_origin(annotation) == typing.Union: + args = typing.get_args(annotation) + + if len(args) == 2 and args[1].__name__ == 'NoneType': + annotation = args[0] + return annotation + +def take_outer_type(annotation): + if typing.get_origin(annotation) is list: + return list + return annotation + + + +def submodel_by_path(model: BaseModel, path:str) -> Type[BaseModel | dict | list]: + """ + Given a pydantic model and an absolute HDF5 path, get the type annotation + """ + parts = path.split('/') + for part in parts: + ann = model.model_fields[part].annotation + + +def flatten_hdf(h5f:h5py.File | h5py.Group, skip='specifications') -> Dict[str, H5SourceItem]: + """ + Flatten all child elements of hdf element into a dict of :class:`.H5SourceItem` s keyed by their path + + Args: + h5f (:class:`h5py.File` | :class:`h5py.Group`): HDF file or group to flatten! + """ + items = {} + def _itemize(name: str, obj: h5py.Dataset | h5py.Group): + if skip in name: + return + leaf = isinstance(obj, h5py.Dataset) or len(obj.keys()) == 0 + # get references in attrs and datasets + refs = [ref for ref in obj.attrs.values() if isinstance(ref, h5py.h5r.Reference)] + if isinstance(obj, h5py.Dataset): + h5_type = 'dataset' + if obj.shape == (): + if isinstance(obj[()], h5py.h5r.Reference): + refs.append(obj[()]) + elif isinstance(obj[0], h5py.h5r.Reference): + refs.extend(obj[:].tolist()) + else: + h5_type = 'group' + # dereference and get name of reference + depends = list(set([h5f[i].name for i in refs])) + items[name] = H5SourceItem.model_construct( + path = name, + leaf = leaf, + depends = depends + ) + + h5f.visititems(_itemize) + return items + diff --git a/nwb_linkml/src/nwb_linkml/providers/schema.py b/nwb_linkml/src/nwb_linkml/providers/schema.py index e2323d8..fa58b8d 100644 --- a/nwb_linkml/src/nwb_linkml/providers/schema.py +++ b/nwb_linkml/src/nwb_linkml/providers/schema.py @@ -172,7 +172,7 @@ class Provider(ABC): return version_path @property - def versions(self) -> Dict[str,List[str]]: + def available_versions(self) -> Dict[str,List[str]]: """ Dictionary mapping a namespace to a list of built versions """ @@ -511,7 +511,7 @@ class PydanticProvider(Provider): path = LinkMLProvider(path=self.config.cache_dir).namespace_path(namespace, version) / 'namespace.yaml' if version is None: # Get the most recently built version - version = LinkMLProvider(path=self.config.cache_dir).versions[name][-1] + version = LinkMLProvider(path=self.config.cache_dir).available_versions[name][-1] fn = path.parts[-1] else: # given a path to a namespace linkml yaml file @@ -563,10 +563,8 @@ class PydanticProvider(Provider): return serialized @classmethod - def module_name(self, namespace:str, version: Optional[str]=None) -> str: - name_pieces = ['nwb_linkml', 'models', 'pydantic', namespace] - if version is not None: - name_pieces.append(version_module_case(version)) + def module_name(self, namespace:str, version: str) -> str: + name_pieces = ['nwb_linkml', 'models', 'pydantic', namespace, version_module_case(version), 'namespace'] module_name = '.'.join(name_pieces) return module_name def import_module( @@ -590,7 +588,7 @@ class PydanticProvider(Provider): """ # get latest version if None if version is None: - version = self.versions[namespace][-1] + version = self.available_versions[namespace][-1] path = self.namespace_path(namespace, version) / 'namespace.py' if not path.exists(): @@ -602,7 +600,9 @@ class PydanticProvider(Provider): spec.loader.exec_module(module) return module - def get(self, namespace: str, version: Optional[str] = None) -> ModuleType: + def get(self, namespace: str, + version: Optional[str] = None, + allow_repo: bool = True) -> ModuleType: """ Get the imported module for a given namespace and version. @@ -628,22 +628,27 @@ class PydanticProvider(Provider): namespace (str): Name of namespace to import. Must have either been previously built with :meth:`.PydanticProvider.build` or a matching namespace/version combo must be available to the :class:`.LinkMLProvider` version (Optional[str]): Version to import. If ``None``, get the most recently build module + allow_repo (bool): Allow getting modules provided within :mod:`nwb_linkml.models.pydantic` Returns: The imported :class:`types.ModuleType` object that has all the built classes at the root level. """ + + if version is None: + version = self.available_versions[namespace][-1] + module_name = self.module_name(namespace, version) if module_name in sys.modules: return sys.modules[module_name] try: - path = self.namespace_path(namespace, version) + path = self.namespace_path(namespace, version, allow_repo) except FileNotFoundError: path = None if path is None or not path.exists(): - _ = self.build(namespace, version) + _ = self.build(namespace, version=version) module = self.import_module(namespace, version) return module @@ -689,6 +694,21 @@ class SchemaProvider(Provider): Alias for :meth:`.LinkMLProvider.build_from_dicts` that also builds a pydantic model """ + def __init__( + self, + versions: Optional[Dict[str, str]] = None, + **kwargs + ): + """ + Args: + versions (dict): Dictionary like ``{'namespace': 'v1.0.0'}`` used to specify that this provider should always + return models from a specific version of a namespace (unless explicitly requested otherwise + in a call to :meth:`.get` ). + **kwargs: passed to superclass __init__ (see :class:`.Provider` ) + """ + self.versions = versions + super(SchemaProvider, self).__init__(**kwargs) + @property def path(self) -> Path: return self.config.cache_dir @@ -737,6 +757,9 @@ class SchemaProvider(Provider): Wrapper around :meth:`.PydanticProvider.get` """ + if version is None and self.versions is not None: + version = self.versions.get(namespace, None) + return PydanticProvider(path=self.path).get(namespace, version) def get_class(self, namespace: str, class_: str, version: Optional[str] = None) -> Type[BaseModel]: @@ -745,6 +768,9 @@ class SchemaProvider(Provider): Wrapper around :meth:`.PydanticProvider.get_class` """ + if version is None and self.versions is not None: + version = self.versions.get(namespace, None) + return PydanticProvider(path=self.path).get_class(namespace, class_, version) diff --git a/nwb_linkml/tests/data/aibs_ecephys.nwb b/nwb_linkml/tests/data/aibs_ecephys.nwb new file mode 100644 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zuG&w*>nHr5)k)@)|A7B#TsjvUWPkq+`|O{`7nj1abcwhI{rl4XTYf46K;NYK3S8f^ z2TGj7Uj?ej}iNcwk~yQ<%5pU;Y~h~+M+K2!34|4n``^Zl01 z*S24rHJ|24^QmT2neT7;d@3=lB4^ff5C8j&_>Z(c`Q+1ls_|6j^YwWnIEwx9o&KFQ NpXxuQ`BW3?{|EA@Y^wkO literal 0 HcmV?d00001 diff --git a/nwb_linkml/tests/data/sources.txt b/nwb_linkml/tests/data/sources.txt new file mode 100644 index 0000000..559aafb --- /dev/null +++ b/nwb_linkml/tests/data/sources.txt @@ -0,0 +1,4 @@ +aibs_ecephys.nwb + - https://dandiarchive.org/dandiset/000021/ + - 000021/sub-738651046/sub-738651046_ses-760693773_probe-769322820_ecephys.nwb + - truncated datasets to length 10 \ No newline at end of file diff --git a/nwb_linkml/tests/test_io/test_io_hdf5.py b/nwb_linkml/tests/test_io/test_io_hdf5.py index 6a336db..6f5cb8a 100644 --- a/nwb_linkml/tests/test_io/test_io_hdf5.py +++ b/nwb_linkml/tests/test_io/test_io_hdf5.py @@ -1,11 +1,14 @@ import pdb +import h5py import pytest from pathlib import Path +import numpy as np from ..fixtures import tmp_output_dir, set_config_vars from nwb_linkml.io.hdf5 import HDF5IO +from nwb_linkml.io.hdf5 import truncate_file @pytest.mark.skip() def test_hdf_read(): NWBFILE = Path('/Users/jonny/Dropbox/lab/p2p_ld/data/nwb/sub-738651046_ses-760693773.nwb') @@ -15,3 +18,73 @@ def test_hdf_read(): model = io.read('acquisition') pdb.set_trace() + +@pytest.mark.skip() +def test_truncate_file(tmp_output_dir): + source = tmp_output_dir / 'truncate_source.hdf5' + + # create a dang ol hdf5 file with a big dataset and some softlinks and make sure + # we truncate the dataset and preserve softlink + + h5f = h5py.File(str(source), 'w') + data_group = h5f.create_group('data') + dataset_contig = h5f.create_dataset( + '/data/dataset_contig', + data=np.zeros((1000,30,40), dtype=np.float64), + compression = "gzip", + compression_opts = 9 + ) + dataset_chunked = h5f.create_dataset( + '/data/dataset_chunked', + data=np.zeros((1000, 40, 50), dtype=np.float64), + compression="gzip", + compression_opts=9, + chunks=True + ) + dataset_contig.attrs['reference_other'] = dataset_chunked.ref + dataset_chunked.attrs['reference_other'] = dataset_contig.ref + dataset_contig.attrs['anattr'] = 1 + + link_group = h5f.create_group('link/child') + link_group.attrs['reference_contig'] = dataset_contig.ref + link_group.attrs['reference_chunked'] = dataset_chunked.ref + h5f.flush() + h5f.close() + + source_size = source.stat().st_size + + # do it without providing target to check that we make filename correctly + n = 10 + target_output = truncate_file(source, n=n) + assert target_output == source.parent / (source.stem + '_truncated.hdf5') + # check that we actually made it smaller + target_size = target_output.stat().st_size + # empirically, the source dataset is ~125KB and truncated is ~17KB + assert target_size < source_size / 5 + + # then check that we have what's expected in the file + target_h5f = h5py.File(target_output, 'r') + + # truncation happened + assert target_h5f['data']['dataset_contig'].shape == (n, 30, 40) + assert target_h5f['data']['dataset_chunked'].shape == (n, 40, 50) + # references still work + # can't directly assess object identity equality with "is" + # so this tests if the referenced dereference and that they dereference to the right place + assert target_h5f[target_h5f['data']['dataset_contig'].attrs['reference_other']].name == target_h5f['data']['dataset_chunked'].name + assert target_h5f[target_h5f['data']['dataset_chunked'].attrs['reference_other']].name == target_h5f['data']['dataset_contig'].name + assert target_h5f[target_h5f['link']['child'].attrs['reference_contig']].name == target_h5f['data']['dataset_contig'].name + assert target_h5f[target_h5f['link']['child'].attrs['reference_chunked']].name == target_h5f['data']['dataset_chunked'].name + assert target_h5f['data']['dataset_contig'].attrs['anattr'] == 1 + +@pytest.mark.skip() +def test_flatten_hdf(): + from nwb_linkml.io.hdf5 import HDF5IO, flatten_hdf + path = '/Users/jonny/Dropbox/lab/p2p_ld/data/nwb/sub-738651046_ses-760693773_probe-769322820_ecephys.nwb' + import h5py + h5f = h5py.File(path) + flat = flatten_hdf(h5f) + assert not any(['specifications' in v.path for v in flat.values()]) + + raise NotImplementedError('Just a stub for local testing for now, finish me!') + diff --git a/nwb_linkml/tests/test_providers/test_provider_schema.py b/nwb_linkml/tests/test_providers/test_provider_schema.py index 392dbf1..e8b0f7d 100644 --- a/nwb_linkml/tests/test_providers/test_provider_schema.py +++ b/nwb_linkml/tests/test_providers/test_provider_schema.py @@ -1,6 +1,8 @@ import pdb import shutil import os +import sys +from pathlib import Path from typing import Optional, Union, List from ..fixtures import tmp_output_dir, set_config_vars @@ -8,6 +10,8 @@ from ..fixtures import tmp_output_dir, set_config_vars import pytest from nwb_linkml.providers.schema import LinkMLProvider, PydanticProvider +import nwb_linkml +from nwb_linkml.maps.naming import version_module_case CORE_MODULES = ( @@ -62,14 +66,24 @@ def test_linkml_provider(tmp_output_dir, repo_version, schema_version, schema_di }) ] ) -def test_pydantic_provider(tmp_output_dir, class_name, test_fields): +def test_pydantic_provider_core(tmp_output_dir, class_name, test_fields): provider = PydanticProvider(path=tmp_output_dir) # clear any prior output assert provider.path.parent == tmp_output_dir shutil.rmtree(provider.path, ignore_errors=True) assert not provider.path.exists() - core = provider.get('core') + # first, we should not build if we're allowed to get core from repo + core = provider.get('core', allow_repo=True) + assert Path(nwb_linkml.__file__).parent in Path(core.__file__).parents + assert not (provider.path / 'core').exists() + + # then, if we're not allowed to get repo versions, we build! + del sys.modules[core.__name__] + core = provider.get('core', allow_repo=False) + # ensure we didn't get the builtin one + assert Path(nwb_linkml.__file__).parent not in Path(core.__file__).parents + assert (tmp_output_dir / 'pydantic' / 'core' / version_module_case(core.version) / 'namespace.py').exists() test_class = getattr(core, class_name) assert test_class == provider.get_class('core', class_name)

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