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figuring out the strategy here...

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- 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
This commit is contained in:
sneakers-the-rat 2023-09-22 00:31:34 -07:00
parent 321740f674
commit 40984a6582
10 changed files with 607 additions and 355 deletions
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3
docs/conf.py
View file

@ -39,7 +39,8 @@ intersphinx_mapping = {
'matplotlib': ('https://matplotlib.org/stable/', None), 'matplotlib': ('https://matplotlib.org/stable/', None),
'numpy': ('https://numpy.org/doc/stable/', None), 'numpy': ('https://numpy.org/doc/stable/', None),
'pandas': ('https://pandas.pydata.org/docs/', None), 'pandas': ('https://pandas.pydata.org/docs/', None),
'pydantic': ('https://docs.pydantic.dev/latest/', None) 'pydantic': ('https://docs.pydantic.dev/latest/', None),
'h5py': ('https://docs.h5py.org/en/stable/', None)
} }

374
nwb_linkml/poetry.lock generated
View file

@ -295,13 +295,13 @@ yaml = ["PyYAML (>=3.10)"]
[[package]] [[package]]
name = "curies" name = "curies"
version = "0.6.0" version = "0.6.3"
description = "Idiomatic conversion between URIs and compact URIs (CURIEs)." description = "Idiomatic conversion between URIs and compact URIs (CURIEs)."
optional = false optional = false
python-versions = ">=3.8" python-versions = ">=3.8"
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] ]
[package.dependencies] [package.dependencies]
@ -397,17 +397,6 @@ files = [
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] ]
[[package]]
name = "decorator"
version = "5.1.1"
description = "Decorators for Humans"
optional = false
python-versions = ">=3.5"
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[[package]] [[package]]
name = "deprecated" name = "deprecated"
version = "1.2.14" version = "1.2.14"
@ -789,20 +778,17 @@ jsonpointer = ">=1.9"
[[package]] [[package]]
name = "jsonpath-ng" name = "jsonpath-ng"
version = "1.5.3" version = "1.6.0"
description = "A final implementation of JSONPath for Python that aims to be standard compliant, including arithmetic and binary comparison operators and providing clear AST for metaprogramming." description = "A final implementation of JSONPath for Python that aims to be standard compliant, including arithmetic and binary comparison operators and providing clear AST for metaprogramming."
optional = false optional = false
python-versions = "*" python-versions = "*"
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[package.dependencies] [package.dependencies]
decorator = "*"
ply = "*" ply = "*"
six = "*"
[[package]] [[package]]
name = "jsonpointer" name = "jsonpointer"
@ -817,13 +803,13 @@ files = [
[[package]] [[package]]
name = "jsonschema" name = "jsonschema"
version = "4.19.0" version = "4.19.1"
description = "An implementation of JSON Schema validation for Python" description = "An implementation of JSON Schema validation for Python"
optional = false optional = false
python-versions = ">=3.8" python-versions = ">=3.8"
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@ -1035,13 +1021,13 @@ files = [
[[package]] [[package]]
name = "nest-asyncio" name = "nest-asyncio"
version = "1.5.7" version = "1.5.8"
description = "Patch asyncio to allow nested event loops" description = "Patch asyncio to allow nested event loops"
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python-versions = ">=3.5" python-versions = ">=3.5"
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[[package]] [[package]]
@ -1170,13 +1156,13 @@ files = [
[[package]] [[package]]
name = "plotly" name = "plotly"
version = "5.16.1" version = "5.17.0"
description = "An open-source, interactive data visualization library for Python" description = "An open-source, interactive data visualization library for Python"
optional = false optional = false
python-versions = ">=3.6" python-versions = ">=3.6"
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[package.dependencies] [package.dependencies]
@ -1486,13 +1472,13 @@ shexjsg = ">=0.8.1"
[[package]] [[package]]
name = "pytest" name = "pytest"
version = "7.4.1" version = "7.4.2"
description = "pytest: simple powerful testing with Python" description = "pytest: simple powerful testing with Python"
optional = false optional = false
python-versions = ">=3.7" python-versions = ">=3.7"
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@ -1816,13 +1802,13 @@ files = [
[[package]] [[package]]
name = "rich" name = "rich"
version = "13.5.2" version = "13.5.3"
description = "Render rich text, tables, progress bars, syntax highlighting, markdown and more to the terminal" description = "Render rich text, tables, progress bars, syntax highlighting, markdown and more to the terminal"
optional = false optional = false
python-versions = ">=3.7.0" python-versions = ">=3.7.0"
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@ -1834,108 +1820,108 @@ jupyter = ["ipywidgets (>=7.5.1,<9)"]
[[package]] [[package]]
name = "rpds-py" name = "rpds-py"
version = "0.10.2" version = "0.10.3"
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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"]
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python-versions = "^3.11" python-versions = "^3.11"
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10
nwb_linkml/pyproject.toml
View file

@ -65,11 +65,11 @@ build-backend = "poetry.core.masonry.api"
[tool.pytest.ini_options] [tool.pytest.ini_options]
addopts = [ #addopts = [
"--cov=nwb_linkml", # "--cov=nwb_linkml",
"--cov-append", # "--cov-append",
"--cov-config=.coveragerc" # "--cov-config=.coveragerc"
] #]
testpaths = [ testpaths = [
"tests", "tests",
'nwb_linkml/tests' 'nwb_linkml/tests'

61
nwb_linkml/src/nwb_linkml/generators/pydantic.py
View file

@ -19,11 +19,12 @@ The `serialize` method
import pdb import pdb
from dataclasses import dataclass, field from dataclasses import dataclass, field
from pathlib import Path 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 import os, sys
from types import ModuleType from types import ModuleType
from copy import deepcopy, copy from copy import deepcopy, copy
import warnings import warnings
import inspect
from nwb_linkml.maps import flat_to_npytyping from nwb_linkml.maps import flat_to_npytyping
from linkml.generators import PydanticGenerator 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 nwb_linkml.maps.naming import module_case, version_module_case
from jinja2 import Template 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", extra_classes:Optional[List[Type[BaseModel]]] = None) -> str:
def default_template(pydantic_ver: str = "1") -> str:
"""Constructs a default template for pydantic classes based on the version of pydantic""" """Constructs a default template for pydantic classes based on the version of pydantic"""
### HEADER ### ### HEADER ###
template = """ template = """
@ -94,7 +99,6 @@ class ConfiguredBaseModel(WeakRefShimBaseModel,
extra = {% if allow_extra %}'allow'{% else %}'forbid'{% endif %}, extra = {% if allow_extra %}'allow'{% else %}'forbid'{% endif %},
arbitrary_types_allowed = True, arbitrary_types_allowed = True,
use_enum_values = True): use_enum_values = True):
pass
""" """
else: else:
template += """ template += """
@ -104,8 +108,22 @@ class ConfiguredBaseModel(BaseModel,
extra = {% if allow_extra %}'allow'{% else %}'forbid'{% endif %}, extra = {% if allow_extra %}'allow'{% else %}'forbid'{% endif %},
arbitrary_types_allowed = True, arbitrary_types_allowed = True,
use_enum_values = 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 ### ### ENUMS ###
template += """ template += """
{% for e in enums.values() %} {% for e in enums.values() %}
@ -142,11 +160,15 @@ class {{ c.name }}
\"\"\" \"\"\"
{%- endif %} {%- endif %}
{% for attr in c.attributes.values() if c.attributes -%} {% 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] }}] Literal[{{ predefined_slot_values[c.name][attr.name] }}]
{%- else -%} {%- else -%}
{{ attr.annotations['python_range'].value }} {{ 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] -%} {%- if predefined_slot_values[c.name][attr.name] -%}
{{ predefined_slot_values[c.name][attr.name] }} {{ predefined_slot_values[c.name][attr.name] }}
{%- elif attr.required -%} {%- elif attr.required -%}
@ -159,6 +181,7 @@ class {{ c.name }}
{%- if attr.minimum_value != None %}, ge={{attr.minimum_value}}{% endif -%} {%- if attr.minimum_value != None %}, ge={{attr.minimum_value}}{% endif -%}
{%- if attr.maximum_value != None %}, le={{attr.maximum_value}}{% endif -%} {%- if attr.maximum_value != None %}, le={{attr.maximum_value}}{% endif -%}
) )
{%- endif %}
{% else -%} {% else -%}
None None
{% endfor %} {% endfor %}
@ -192,6 +215,9 @@ class NWBPydanticGenerator(PydanticGenerator):
# SKIP_SLOTS=('VectorData',) # SKIP_SLOTS=('VectorData',)
SKIP_SLOTS=('',) SKIP_SLOTS=('',)
SKIP_CLASSES=('',) 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') # SKIP_CLASSES=('VectorData','VectorIndex')
split:bool=True split:bool=True
schema_map:Optional[Dict[str, SchemaDefinition]]=None schema_map:Optional[Dict[str, SchemaDefinition]]=None
@ -410,6 +436,18 @@ class NWBPydanticGenerator(PydanticGenerator):
union += '\n' + ' '*4 + ']' union += '\n' + ' '*4 + ']'
return union 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]: 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: with open(self.template_file) as template_file:
template_obj = Template(template_file.read()) template_obj = Template(template_file.read())
else: else:
template_obj = Template(default_template(self.pydantic_version)) template_obj = Template(default_template(self.pydantic_version,
extra_classes=[LinkML_Meta]))
sv: SchemaView sv: SchemaView
sv = self.schemaview sv = self.schemaview
@ -581,6 +620,9 @@ class NWBPydanticGenerator(PydanticGenerator):
for attribute in list(class_def.attributes.keys()): for attribute in list(class_def.attributes.keys()):
del class_def.attributes[attribute] 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 class_name = class_original.name
predefined_slot_values[camelcase(class_name)] = {} predefined_slot_values[camelcase(class_name)] = {}
for s in class_slots[class_name]: for s in class_slots[class_name]:
@ -632,6 +674,7 @@ class NWBPydanticGenerator(PydanticGenerator):
pyrange = f"Optional[{pyrange}]" pyrange = f"Optional[{pyrange}]"
ann = Annotation("python_range", pyrange) ann = Annotation("python_range", pyrange)
s.annotations[ann.tag] = ann s.annotations[ann.tag] = ann
code = template_obj.render( code = template_obj.render(
imports=imports, imports=imports,
schema=pyschema, schema=pyschema,
@ -642,6 +685,7 @@ class NWBPydanticGenerator(PydanticGenerator):
metamodel_version=self.schema.metamodel_version, metamodel_version=self.schema.metamodel_version,
version=self.schema.version, version=self.schema.version,
class_isa_plus_mixins=self.get_class_isa_plus_mixins(sorted_classes), class_isa_plus_mixins=self.get_class_isa_plus_mixins(sorted_classes),
injected_fields=self.INJECTED_FIELDS
) )
return code return code
@ -678,3 +722,4 @@ def compile_python(text_or_fn: str, package_path: Path = None) -> ModuleType: #
exec(spec, module.__dict__) exec(spec, module.__dict__)
return module return module

373
nwb_linkml/src/nwb_linkml/io/hdf5.py
View file

@ -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 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 pdb
import typing import typing
import warnings
from typing import Optional, List, Dict, overload, Literal, Type, Any from typing import Optional, List, Dict, overload, Literal, Type, Any
from pathlib import Path from pathlib import Path
from types import ModuleType from types import ModuleType
from typing import TypeVar, TYPE_CHECKING from typing import TypeVar, TYPE_CHECKING, NamedTuple
from abc import abstractmethod from abc import abstractmethod
import json import json
import subprocess
import shutil
import h5py import h5py
from pydantic import BaseModel from pydantic import BaseModel, Field, ConfigDict
from dataclasses import dataclass, field from dataclasses import dataclass, field
from nwb_linkml.translate import generate_from_nwbfile from nwb_linkml.translate import generate_from_nwbfile
#from nwb_linkml.models.core_nwb_file import NWBFile #from nwb_linkml.models.core_nwb_file import NWBFile
if TYPE_CHECKING: 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 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 class H5SourceItem(BaseModel):
def read(self) -> BaseModel | List[BaseModel]: """Tuple of items for each element when flattening an hdf5 file"""
""" path: str
Constructs the pydantic model from the given hdf5 element """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 @property
def name(self) -> str: def parts(self) -> List[str]:
"""Just the terminal group name""" """path split by /"""
return self.cls.name.split('/')[-1] return self.path.split('/')
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())
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(): class HDF5IO():
@ -169,19 +94,23 @@ class HDF5IO():
def read(self, path:str) -> BaseModel | Dict[str, BaseModel]: ... def read(self, path:str) -> BaseModel | Dict[str, BaseModel]: ...
def read(self, path:Optional[str] = None): def read(self, path:Optional[str] = None):
provider = self.make_provider()
h5f = h5py.File(str(self.path)) 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: if path:
src = h5f.get(path) src = h5f.get(path)
parent = get_model(src)
else: else:
src = h5f 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 = {} data = {}
for k, v in src.items(): 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: def process_group(self, group:h5py.Group|h5py.File) -> dict | list:
attrs = dict(group.attrs) 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 = {} spec_dict = {}
def _read_spec(name, node): 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 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

46
nwb_linkml/src/nwb_linkml/providers/schema.py
View file

@ -172,7 +172,7 @@ class Provider(ABC):
return version_path return version_path
@property @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 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' path = LinkMLProvider(path=self.config.cache_dir).namespace_path(namespace, version) / 'namespace.yaml'
if version is None: if version is None:
# Get the most recently built version # 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] fn = path.parts[-1]
else: else:
# given a path to a namespace linkml yaml file # given a path to a namespace linkml yaml file
@ -563,10 +563,8 @@ class PydanticProvider(Provider):
return serialized return serialized
@classmethod @classmethod
def module_name(self, namespace:str, version: Optional[str]=None) -> str: def module_name(self, namespace:str, version: str) -> str:
name_pieces = ['nwb_linkml', 'models', 'pydantic', namespace] name_pieces = ['nwb_linkml', 'models', 'pydantic', namespace, version_module_case(version), 'namespace']
if version is not None:
name_pieces.append(version_module_case(version))
module_name = '.'.join(name_pieces) module_name = '.'.join(name_pieces)
return module_name return module_name
def import_module( def import_module(
@ -590,7 +588,7 @@ class PydanticProvider(Provider):
""" """
# get latest version if None # get latest version if None
if version is None: if version is None:
version = self.versions[namespace][-1] version = self.available_versions[namespace][-1]
path = self.namespace_path(namespace, version) / 'namespace.py' path = self.namespace_path(namespace, version) / 'namespace.py'
if not path.exists(): if not path.exists():
@ -602,7 +600,9 @@ class PydanticProvider(Provider):
spec.loader.exec_module(module) spec.loader.exec_module(module)
return 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. 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 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` 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 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: Returns:
The imported :class:`types.ModuleType` object that has all the built classes at the root level. 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) module_name = self.module_name(namespace, version)
if module_name in sys.modules: if module_name in sys.modules:
return sys.modules[module_name] return sys.modules[module_name]
try: try:
path = self.namespace_path(namespace, version) path = self.namespace_path(namespace, version, allow_repo)
except FileNotFoundError: except FileNotFoundError:
path = None path = None
if path is None or not path.exists(): if path is None or not path.exists():
_ = self.build(namespace, version) _ = self.build(namespace, version=version)
module = self.import_module(namespace, version) module = self.import_module(namespace, version)
return module return module
@ -689,6 +694,21 @@ class SchemaProvider(Provider):
Alias for :meth:`.LinkMLProvider.build_from_dicts` that also builds a pydantic model 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 @property
def path(self) -> Path: def path(self) -> Path:
return self.config.cache_dir return self.config.cache_dir
@ -737,6 +757,9 @@ class SchemaProvider(Provider):
Wrapper around :meth:`.PydanticProvider.get` 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) return PydanticProvider(path=self.path).get(namespace, version)
def get_class(self, namespace: str, class_: str, version: Optional[str] = None) -> Type[BaseModel]: 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` 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) return PydanticProvider(path=self.path).get_class(namespace, class_, version)

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nwb_linkml/tests/data/aibs_ecephys.nwb Normal file
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4
nwb_linkml/tests/data/sources.txt Normal file
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@ -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

73
nwb_linkml/tests/test_io/test_io_hdf5.py
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@ -1,11 +1,14 @@
import pdb import pdb
import h5py
import pytest import pytest
from pathlib import Path from pathlib import Path
import numpy as np
from ..fixtures import tmp_output_dir, set_config_vars from ..fixtures import tmp_output_dir, set_config_vars
from nwb_linkml.io.hdf5 import HDF5IO from nwb_linkml.io.hdf5 import HDF5IO
from nwb_linkml.io.hdf5 import truncate_file
@pytest.mark.skip() @pytest.mark.skip()
def test_hdf_read(): def test_hdf_read():
NWBFILE = Path('/Users/jonny/Dropbox/lab/p2p_ld/data/nwb/sub-738651046_ses-760693773.nwb') 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') model = io.read('acquisition')
pdb.set_trace() 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!')

18
nwb_linkml/tests/test_providers/test_provider_schema.py
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@ -1,6 +1,8 @@
import pdb import pdb
import shutil import shutil
import os import os
import sys
from pathlib import Path
from typing import Optional, Union, List from typing import Optional, Union, List
from ..fixtures import tmp_output_dir, set_config_vars from ..fixtures import tmp_output_dir, set_config_vars
@ -8,6 +10,8 @@ from ..fixtures import tmp_output_dir, set_config_vars
import pytest import pytest
from nwb_linkml.providers.schema import LinkMLProvider, PydanticProvider from nwb_linkml.providers.schema import LinkMLProvider, PydanticProvider
import nwb_linkml
from nwb_linkml.maps.naming import version_module_case
CORE_MODULES = ( 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) provider = PydanticProvider(path=tmp_output_dir)
# clear any prior output # clear any prior output
assert provider.path.parent == tmp_output_dir assert provider.path.parent == tmp_output_dir
shutil.rmtree(provider.path, ignore_errors=True) shutil.rmtree(provider.path, ignore_errors=True)
assert not provider.path.exists() 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) test_class = getattr(core, class_name)
assert test_class == provider.get_class('core', class_name) assert test_class == provider.get_class('core', class_name)