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object wrapper for zip files" -groups = ["default", "dev", "plot", "tests"] +groups = ["dev", "plot", "tests"] files = [ {file = "zipp-3.19.2-py3-none-any.whl", hash = "sha256:f091755f667055f2d02b32c53771a7a6c8b47e1fdbc4b72a8b9072b3eef8015c"}, {file = "zipp-3.19.2.tar.gz", hash = "sha256:bf1dcf6450f873a13e952a29504887c89e6de7506209e5b1bcc3460135d4de19"}, diff --git a/nwb_linkml/pyproject.toml b/nwb_linkml/pyproject.toml index 8455bed..6efc111 100644 --- a/nwb_linkml/pyproject.toml +++ b/nwb_linkml/pyproject.toml @@ -15,11 +15,9 @@ dependencies = [ "rich>=13.5.2", #"linkml>=1.7.10", "linkml @ git+https://github.com/sneakers-the-rat/linkml@nwb-linkml", - "nptyping>=2.5.0", "pydantic>=2.3.0", "h5py>=3.9.0", "pydantic-settings>=2.0.3", - "dask>=2023.9.2", "tqdm>=4.66.1", 'typing-extensions>=4.12.2;python_version<"3.11"', "numpydantic>=1.3.0", diff --git a/nwb_linkml/src/nwb_linkml/maps/__init__.py b/nwb_linkml/src/nwb_linkml/maps/__init__.py index cb7f329..8b01447 100644 --- a/nwb_linkml/src/nwb_linkml/maps/__init__.py +++ b/nwb_linkml/src/nwb_linkml/maps/__init__.py @@ -2,7 +2,7 @@ Mapping from one domain to another """ -from nwb_linkml.maps.dtype import flat_to_linkml, flat_to_np, flat_to_nptyping +from nwb_linkml.maps.dtype import flat_to_linkml, flat_to_np from nwb_linkml.maps.map import Map from nwb_linkml.maps.postload import MAP_HDMF_DATATYPE_DEF, MAP_HDMF_DATATYPE_INC from nwb_linkml.maps.quantity import QUANTITY_MAP @@ -14,5 +14,4 @@ __all__ = [ "Map", "flat_to_linkml", "flat_to_np", - "flat_to_nptyping", ] diff --git a/nwb_linkml/src/nwb_linkml/maps/dtype.py b/nwb_linkml/src/nwb_linkml/maps/dtype.py index 26d25a6..1c90e0d 100644 --- a/nwb_linkml/src/nwb_linkml/maps/dtype.py +++ b/nwb_linkml/src/nwb_linkml/maps/dtype.py @@ -3,9 +3,8 @@ Dtype mappings """ from datetime import datetime -from typing import Any, Type +from typing import Any -import nptyping import numpy as np from nwb_schema_language import CompoundDtype, DTypeType, FlatDtype, ReferenceDtype @@ -40,37 +39,6 @@ flat_to_linkml = { Map between the flat data types and the simpler linkml base types """ -flat_to_nptyping = { - "float": "Float", - "float32": "Float32", - "double": "Double", - "float64": "Float64", - "long": "LongLong", - "int64": "Int64", - "int": "Int", - "int32": "Int32", - "int16": "Int16", - "short": "Short", - "int8": "Int8", - "uint": "UInt", - "uint32": "UInt32", - "uint16": "UInt16", - "uint8": "UInt8", - "uint64": "UInt64", - "numeric": "Number", - "text": "String", - "utf": "Unicode", - "utf8": "Unicode", - "utf_8": "Unicode", - "string": "Unicode", - "str": "Unicode", - "ascii": "String", - "bool": "Bool", - "isodatetime": "Datetime64", - "AnyType": "Any", - "object": "Object", -} - flat_to_np = { "float": float, "float32": np.float32, @@ -175,20 +143,6 @@ https://github.com/hdmf-dev/hdmf/blob/ddc842b5c81d96e0b957b96e88533b16c137e206/s """ -def struct_from_dtype(dtype: np.dtype) -> Type[nptyping.Structure]: - """ - Create a nptyping Structure from a compound numpy dtype - - nptyping structures have the form:: - - Structure["name: Str, age: Int"] - - """ - struct_pieces = [f"{k}: {flat_to_nptyping[v[0].name]}" for k, v in dtype.fields.items()] - struct_dtype = ", ".join(struct_pieces) - return nptyping.Structure[struct_dtype] - - def handle_dtype(dtype: DTypeType | None) -> str: """ Get the string form of a dtype diff --git a/nwb_linkml/src/nwb_linkml/maps/hdmf.py b/nwb_linkml/src/nwb_linkml/maps/hdmf.py index b2c552d..3c52b22 100644 --- a/nwb_linkml/src/nwb_linkml/maps/hdmf.py +++ b/nwb_linkml/src/nwb_linkml/maps/hdmf.py @@ -2,69 +2,9 @@ Mapping functions for handling HDMF classes like DynamicTables """ -from typing import Any, List, Optional, Type +from typing import Any, List, Optional -import dask.array as da import h5py -import numpy as np -from numpydantic import NDArray -from numpydantic.interface.hdf5 import H5ArrayPath -from pydantic import BaseModel, create_model - -from nwb_linkml.maps.dtype import struct_from_dtype -from nwb_linkml.types.hdf5 import HDF5_Path - - -def model_from_dynamictable(group: h5py.Group, base: Optional[BaseModel] = None) -> Type[BaseModel]: - """ - Create a pydantic model from a dynamic table - """ - colnames = group.attrs["colnames"] - types = {} - for col in colnames: - - nptype = group[col].dtype - nptype = struct_from_dtype(nptype) if nptype.type == np.void else nptype.type - - type_ = Optional[NDArray[Any, nptype]] - - # FIXME: handling nested column types that appear only in some versions? - # types[col] = (List[type_ | None], ...) - types[col] = (type_, None) - - model = create_model(group.name.split("/")[-1], **types, __base__=base) - return model - - -def dynamictable_to_model( - group: h5py.Group, - model: Optional[Type[BaseModel]] = None, - base: Optional[Type[BaseModel]] = None, -) -> BaseModel: - """ - Instantiate a dynamictable model - - Calls :func:`.model_from_dynamictable` if ``model`` is not provided. - """ - if model is None: - model = model_from_dynamictable(group, base) - - items = {} - for col, col_type in model.model_fields.items(): - if col not in group: - if col in group.attrs: - items[col] = group.attrs[col] - continue - - if col_type.annotation is HDF5_Path: - items[col] = [HDF5_Path(group[d].name) for d in group[col][:]] - else: - try: - items[col] = da.from_array(group[col]) - except NotImplementedError: - items[col] = H5ArrayPath(file=group.file.filename, path=group[col].name) - - return model.model_construct(hdf5_path=group.name, name=group.name.split("/")[-1], **items) def dereference_reference_vector(dset: h5py.Dataset, data: Optional[List[Any]]) -> List: diff --git a/nwb_linkml/src/nwb_linkml/monkeypatch.py b/nwb_linkml/src/nwb_linkml/monkeypatch.py index d9da2c5..6222089 100644 --- a/nwb_linkml/src/nwb_linkml/monkeypatch.py +++ b/nwb_linkml/src/nwb_linkml/monkeypatch.py @@ -5,56 +5,6 @@ Monkeypatches to external modules # ruff: noqa: ANN001 - not well defined types for this module -def patch_npytyping_perf() -> None: - """ - npytyping makes an expensive call to inspect.stack() - that makes imports of pydantic models take ~200x longer than - they should: - - References: - - https://github.com/ramonhagenaars/nptyping/issues/110 - """ - import inspect - from types import FrameType - - from nptyping import base_meta_classes, ndarray, recarray - from nptyping.pandas_ import dataframe - - # make a new __module__ methods for the affected classes - def new_module_ndarray(cls) -> str: - return cls._get_module(inspect.currentframe(), "nptyping.ndarray") - - def new_module_recarray(cls) -> str: - return cls._get_module(inspect.currentframe(), "nptyping.recarray") - - def new_module_dataframe(cls) -> str: - return cls._get_module(inspect.currentframe(), "nptyping.pandas_.dataframe") - - # and a new _get_module method for the parent class - def new_get_module(cls, stack: FrameType, module: str) -> str: - return ( - "typing" - if inspect.getframeinfo(stack.f_back).function == "formatannotation" - else module - ) - - # now apply the patches - ndarray.NDArrayMeta.__module__ = property(new_module_ndarray) - recarray.RecArrayMeta.__module__ = property(new_module_recarray) - dataframe.DataFrameMeta.__module__ = property(new_module_dataframe) - base_meta_classes.SubscriptableMeta._get_module = new_get_module - - -def patch_nptyping_warnings() -> None: - """ - nptyping shits out a bunch of numpy deprecation warnings from using - olde aliases - """ - import warnings - - warnings.filterwarnings("ignore", category=DeprecationWarning, module="nptyping.*") - - def patch_schemaview() -> None: """ Patch schemaview to correctly resolve multiple layers of relative imports. @@ -211,8 +161,6 @@ def patch_pretty_print() -> None: def apply_patches() -> None: """Apply all monkeypatches""" - patch_npytyping_perf() - patch_nptyping_warnings() patch_schemaview() patch_array_expression() patch_pretty_print() diff --git a/nwb_linkml/tests/test_maps/test_dtype.py b/nwb_linkml/tests/test_maps/test_dtype.py deleted file mode 100644 index 569b262..0000000 --- a/nwb_linkml/tests/test_maps/test_dtype.py +++ /dev/null @@ -1,11 +0,0 @@ -import nptyping -import numpy as np - -from nwb_linkml.maps.dtype import struct_from_dtype - - -def test_struct_from_dtype(): - # Super weak test with fixed values, will expand with parameterize if needed - np_dtype = np.dtype([("name1", "int32"), ("name2", "object"), ("name3", "str")]) - struct = struct_from_dtype(np_dtype) - assert struct == nptyping.Structure["name1: Int32, name2: Object, name3: Unicode"]